OML4Py Clustering EM.dsnb

[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py Clustering EM","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1713804680496,"interpreter":"md.low","endTime":1713804680570,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Identifying Customer Segments using Expectation Maximization Clustering","Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.","","See the documentation link below for details on the Expectation Maximization in-database algortihm. ","","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1713804680717,"interpreter":"md.low","endTime":1713804680797,"results":[{"message":"<h1 id=\"identifying-customer-segments-using-expectation-maximization-clustering\">Identifying Customer Segments using Expectation Maximization Clustering<\/h1>\n<p>Oracle Machine Learning supports clustering using several algorithms: k-Means, O-Cluster, and Expectation Maximization. In this notebook, we illustrate how to identify natural clusters of customers using the CUSTOMERS dataset from the SH schema using the unsupervised learning Expectation Maximization (EM) algorithm. The data exploration, preparation, and machine learning run inside Oracle Autonomous Database.<\/p>\n<p>See the documentation link below for details on the Expectation Maximization in-database algortihm.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","![tiny arrow](http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg \"tiny arrow\")"],"enabled":true,"result":{"startTime":1713804680886,"interpreter":"md.low","endTime":1713804680948,"results":[{"message":"<p><img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/clustering-5663171.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a>","* <a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a>","* <a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a>"],"enabled":true,"result":{"startTime":1715292393044,"interpreter":"md.low","endTime":1715292393196,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADW Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle/oracle-db-examples/tree/master/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-clustering\" target=\"_blank\">OML Clustering<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-expectation-maximization\" target=\"_blank\">OML Expectation Maximization<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2/mlpug/expectation-maximization.html\" target=\"_blank\">OML4Py Expectation Maximization<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python\r","\r","import pandas as pd\r","import numpy as np\r","import matplotlib.pyplot as plt\r","import oml\r","\r","pd.set_option('display.max_rows', 500)\r","pd.set_option('display.max_columns', 500)\r","pd.set_option('display.width', 1000)"],"enabled":true,"result":{"startTime":1713804681241,"interpreter":"python.low","endTime":1713804681322,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":5,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Prepare data by merging CUSTOMERS with SUPPLEMENTARY_DEMOGRAPHICS","message":["%python","","CUSTOMERS = oml.sync(query = \"\"\"SELECT CUST_ID, CUST_GENDER, CUST_MARITAL_STATUS, CUST_YEAR_OF_BIRTH, CUST_INCOME_LEVEL, CUST_CREDIT_LIMIT "," FROM SH.CUSTOMERS\"\"\")","DEMO_DF = oml.sync(query = \"\"\"SELECT CUST_ID, EDUCATION, AFFINITY_CARD, HOUSEHOLD_SIZE, OCCUPATION, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS\"\"\")","CUST_DF = CUSTOMERS.merge(DEMO_DF, how = \"inner\", on = 'CUST_ID',suffixes = [\"\",\"\"])"],"enabled":true,"result":{"startTime":1713804681403,"interpreter":"python.low","endTime":1713804681503,"results":[{"message":"<stdin>:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the first 5 rows of CUST_DF data","message":["%python","","z.show(CUST_DF.head())"],"enabled":true,"result":{"startTime":1713804681590,"interpreter":"python.low","endTime":1713804681752,"results":[{"message":"CUST_ID\tCUST_GENDER\tCUST_MARITAL_STATUS\tCUST_YEAR_OF_BIRTH\tCUST_INCOME_LEVEL\tCUST_CREDIT_LIMIT\tEDUCATION\tAFFINITY_CARD\tHOUSEHOLD_SIZE\tOCCUPATION\tYRS_RESIDENCE\tY_BOX_GAMES\n100134\tF\tDivorc.\t1965\tL: 300,000 and above\t9000\tAssoc-A\t0\t2\tCleric.\t2\t0\n102828\tF\tNeverM\t1967\tE: 90,000 - 109,999\t10000\tHS-grad\t0\t1\tMachine\t4\t0\n101232\tM\tNeverM\t1979\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tOther\t2\t1\n100696\tM\tMarried\t1971\tF: 110,000 - 129,999\t7000\tProfsc\t1\t3\tProf.\t3\t0\n103948\tM\tNeverM\t1966\tJ: 190,000 - 249,999\t9000\t< Bach.\t0\t1\tCleric.\t4\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Build an Expectation Maximization model using the CUST_DF proxy object"],"enabled":true,"result":{"startTime":1713804681868,"interpreter":"md.low","endTime":1713804681928,"results":[{"message":"<h3 id=\"build-an-expectation-maximization-model-using-the-cust_df-proxy-object\">Build an Expectation Maximization model using the CUST_DF proxy object<\/h3>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with default settings","message":["%python","","try:"," oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:"," pass","","setting = {'emcs_num_iterations': 20, 'emcs_random_seed': 7}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, "," model_name = \"CUST_CLUSTER_MODEL\", "," case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713804682003,"interpreter":"python.low","endTime":1713804688810,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Build EM clustering model with 3 clusters with explicit settings","message":["%python","","try:"," oml.drop(model=\"CUST_CLUSTER_MODEL\")","except:"," pass","","setting = {'EMCS_NUM_ITERATIONS': 20,"," 'EMCS_RANDOM_SEED': 7,"," 'EMCS_CLUSTER_COMPONENTS': 'EMCS_CLUSTER_COMP_ENABLE',"," 'EMCS_CLUSTER_STATISTICS': 'EMCS_CLUS_STATS_ENABLE',"," 'EMCS_CLUSTER_THRESH': 2,"," 'EMCS_LINKAGE_FUNCTION': 'EMCS_LINKAGE_SINGLE',"," 'EMCS_LOGLIKE_IMPROVEMENT': .001,"," 'EMCS_MAX_NUM_ATTR_2D': 50,"," 'EMCS_MIN_PCT_ATTR_SUPPORT': .1,"," 'EMCS_MODEL_SEARCH': 'EMCS_MODEL_SEARCH_DISABLE',"," 'EMCS_NUM_COMPONENTS': 20,"," 'EMCS_NUM_DISTRIBUTION': 'EMCS_NUM_DISTR_SYSTEM',"," 'EMCS_NUM_EQUIWIDTH_BINS': 11,"," 'EMCS_NUM_PROJECTIONS': 50,"," 'EMCS_REMOVE_COMPONENTS': 'EMCS_REMOVE_COMPS_ENABLE',"," 'EMCS_ATTRIBUTE_FILTER': 'EMCS_ATTR_FILTER_ENABLE',"," 'EMCS_CONVERGENCE_CRITERION': 'EMCS_CONV_CRIT_HELDASIDE',"," 'EMCS_NUM_QUANTILE_BINS': 11,"," 'EMCS_NUM_TOPN_BINS': 15,"," 'ODMS_DETAILS': 'ODMS_ENABLE',"," 'ODMS_SAMPLING': 'ODMS_SAMPLING_DISABLE',"," 'PREP_AUTO': 'ON'}","em_mod = oml.em(n_clusters = 3, **setting).fit(CUST_DF, model_name = \"CUST_CLUSTER_MODEL\", case_id = 'CUST_ID')"],"enabled":true,"result":{"startTime":1713804688886,"interpreter":"python.low","endTime":1713804694391,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Display Expectation Maximization model details from model proxy object","message":["%python","","em_mod"],"enabled":true,"result":{"startTime":1713804694467,"interpreter":"python.low","endTime":1713804695302,"results":[{"message":"\nModel Name: CUST_CLUSTER_MODEL\n\nModel Owner: OMLUSER\n\nAlgorithm Name: Expectation Maximization\n\nMining Function: CLUSTERING\n\nSettings: \n setting name setting value\n0 ALGO_NAME ALGO_EXPECTATION_MAXIMIZATION\n1 CLUS_NUM_CLUSTERS 3\n2 EMCS_ATTRIBUTE_FILTER EMCS_ATTR_FILTER_ENABLE\n3 EMCS_CLUSTER_COMPONENTS EMCS_CLUSTER_COMP_ENABLE\n4 EMCS_CLUSTER_STATISTICS EMCS_CLUS_STATS_ENABLE\n5 EMCS_CLUSTER_THRESH 2\n6 EMCS_CONVERGENCE_CRITERION EMCS_CONV_CRIT_HELDASIDE\n7 EMCS_LINKAGE_FUNCTION EMCS_LINKAGE_SINGLE\n8 EMCS_LOGLIKE_IMPROVEMENT 0.001\n9 EMCS_MAX_NUM_ATTR_2D 50\n10 EMCS_MIN_PCT_ATTR_SUPPORT 0.1\n11 EMCS_MODEL_SEARCH EMCS_MODEL_SEARCH_DISABLE\n12 EMCS_NUM_COMPONENTS 20\n13 EMCS_NUM_DISTRIBUTION EMCS_NUM_DISTR_SYSTEM\n14 EMCS_NUM_EQUIWIDTH_BINS 11\n15 EMCS_NUM_ITERATIONS 20\n16 EMCS_NUM_PROJECTIONS 50\n17 EMCS_NUM_QUANTILE_BINS 11\n18 EMCS_NUM_TOPN_BINS 15\n19 EMCS_RANDOM_SEED 7\n20 EMCS_REMOVE_COMPONENTS EMCS_REMOVE_COMPS_ENABLE\n21 ODMS_DETAILS ODMS_ENABLE\n22 ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO\n23 ODMS_SAMPLING ODMS_SAMPLING_DISABLE\n24 PREP_AUTO ON\n\nGlobal Statistics: \n attribute name attribute value\n0 CONVERGED NO\n1 LOGLIKELIHOOD -13.5244\n2 NUM_CLUSTERS 3\n3 NUM_COMPONENTS 20\n4 NUM_ROWS 4500\n5 RANDOM_SEED 7\n6 REMOVED_COMPONENTS 0\n\nAttributes: \nAFFINITY_CARD\nCUST_CREDIT_LIMIT\nCUST_GENDER\nCUST_INCOME_LEVEL\nCUST_MARITAL_STATUS\nCUST_YEAR_OF_BIRTH\nEDUCATION\nHOUSEHOLD_SIZE\nOCCUPATION\nYRS_RESIDENCE\nY_BOX_GAMES\n\nPartition: NO\n\nClusters: \n\n CLUSTER_ID CLUSTER_NAME RECORD_COUNT PARENT TREE_LEVEL LEFT_CHILD_ID RIGHT_CHILD_ID\n0 1 1 4500 NaN 1 2.0 3.0\n1 2 2 1164 1.0 2 NaN NaN\n2 3 3 3336 1.0 2 4.0 5.0\n3 4 4 3155 3.0 3 NaN NaN\n4 5 5 181 3.0 3 NaN NaN\n\nTaxonomy: \n\n PARENT_CLUSTER_ID CHILD_CLUSTER_ID\n0 1 2.0\n1 1 3.0\n2 2 NaN\n3 3 4.0\n4 3 5.0\n5 4 NaN\n6 5 NaN\n\nCentroids: \n\n CLUSTER_ID ATTRIBUTE_NAME MEAN MODE_VALUE VARIANCE\n0 1 AFFINITY_CARD 0.238222 None 1.815127e-01\n1 1 CUST_CREDIT_LIMIT 7924.222222 None 1.591424e+07\n2 1 CUST_GENDER NaN M NaN\n3 1 CUST_INCOME_LEVEL NaN J: 190,000 - 249,999 NaN\n4 1 CUST_MARITAL_STATUS NaN Married NaN\n5 1 CUST_YEAR_OF_BIRTH 1964.624444 None 1.871268e+02\n6 1 EDUCATION NaN HS-grad NaN\n7 1 HOUSEHOLD_SIZE NaN 3 NaN\n8 1 OCCUPATION NaN Crafts NaN\n9 1 YRS_RESIDENCE 4.022000 None 3.617431e+00\n10 1 Y_BOX_GAMES 0.312444 None 2.148707e-01\n11 2 AFFINITY_CARD 0.016323 None 1.607039e-02\n12 2 CUST_CREDIT_LIMIT 8265.034364 None 1.567110e+07\n13 2 CUST_GENDER NaN M NaN\n14 2 CUST_INCOME_LEVEL NaN J: 190,000 - 249,999 NaN\n15 2 CUST_MARITAL_STATUS NaN NeverM NaN\n16 2 CUST_YEAR_OF_BIRTH 1980.033505 None 1.136517e+01\n17 2 EDUCATION NaN < Bach. NaN\n18 2 HOUSEHOLD_SIZE NaN 1 NaN\n19 2 OCCUPATION NaN Other NaN\n20 2 YRS_RESIDENCE 2.134880 None 8.579741e-01\n21 2 Y_BOX_GAMES 1.000000 None 0.000000e+00\n22 3 AFFINITY_CARD 0.315647 None 2.160789e-01\n23 3 CUST_CREDIT_LIMIT 7805.305755 None 1.594911e+07\n24 3 CUST_GENDER NaN M NaN\n25 3 CUST_INCOME_LEVEL NaN J: 190,000 - 249,999 NaN\n26 3 CUST_MARITAL_STATUS NaN Married NaN\n27 3 CUST_YEAR_OF_BIRTH 1959.247902 None 1.366873e+02\n28 3 EDUCATION NaN HS-grad NaN\n29 3 HOUSEHOLD_SIZE NaN 3 NaN\n30 3 OCCUPATION NaN Exec. NaN\n31 3 YRS_RESIDENCE 4.680456 None 2.904158e+00\n32 3 Y_BOX_GAMES 0.072542 None 6.729980e-02\n33 4 AFFINITY_CARD 0.301743 None 2.107611e-01\n34 4 CUST_CREDIT_LIMIT 7799.683043 None 1.589185e+07\n35 4 CUST_GENDER NaN M NaN\n36 4 CUST_INCOME_LEVEL NaN J: 190,000 - 249,999 NaN\n37 4 CUST_MARITAL_STATUS NaN Married NaN\n38 4 CUST_YEAR_OF_BIRTH 1959.118225 None 1.406021e+02\n39 4 EDUCATION NaN HS-grad NaN\n40 4 HOUSEHOLD_SIZE NaN 3 NaN\n41 4 OCCUPATION NaN Crafts NaN\n42 4 YRS_RESIDENCE 4.721078 None 2.919641e+00\n43 4 Y_BOX_GAMES 0.076704 None 7.084265e-02\n44 5 AFFINITY_CARD 0.558011 None 2.480049e-01\n45 5 CUST_CREDIT_LIMIT 7903.314917 None 1.703088e+07\n46 5 CUST_GENDER NaN F NaN\n47 5 CUST_INCOME_LEVEL NaN J: 190,000 - 249,999 NaN\n48 5 CUST_MARITAL_STATUS NaN Married NaN\n49 5 CUST_YEAR_OF_BIRTH 1961.508287 None 6.341799e+01\n50 5 EDUCATION NaN HS-grad NaN\n51 5 HOUSEHOLD_SIZE NaN 4-5 NaN\n52 5 OCCUPATION NaN Cleric. NaN\n53 5 YRS_RESIDENCE 3.972376 None 2.115899e+00\n54 5 Y_BOX_GAMES 0.000000 None 0.000000e+00\n\nLeaf Cluster Counts: \n\n CLUSTER_ID CNT\n0 2 1164\n1 4 3155\n2 5 181\n\nAttribute Importance: \n\n ATTRIBUTE_NAME ATTRIBUTE_IMPORTANCE_VALUE ATTRIBUTE_RANK\n0 AFFINITY_CARD 0.125510 7\n1 CUST_CREDIT_LIMIT 0.083163 10\n2 CUST_GENDER 0.187811 6\n3 CUST_INCOME_LEVEL 0.087468 8\n4 CUST_MARITAL_STATUS 0.232739 4\n5 CUST_YEAR_OF_BIRTH 0.368715 2\n6 EDUCATION 0.057243 11\n7 HOUSEHOLD_SIZE 0.263308 3\n8 OCCUPATION 0.085454 9\n9 YRS_RESIDENCE 0.232491 5\n10 Y_BOX_GAMES 0.411961 1\n\nComponents: \n\n COMPONENT_ID CLUSTER_ID PRIOR_PROBABILITY\n0 1 2 0.083151\n1 2 4 0.041165\n2 3 4 0.025857\n3 4 2 0.055326\n4 5 2 0.032430\n5 6 4 0.037727\n6 7 4 0.045825\n7 8 2 0.044490\n8 9 4 0.081876\n9 10 4 0.072249\n10 11 4 0.040540\n11 12 4 0.052175\n12 13 4 0.051327\n13 14 5 0.041011\n14 15 4 0.055620\n15 16 4 0.051606\n16 17 4 0.042597\n17 18 4 0.052004\n18 19 4 0.052609\n19 20 2 0.040414\n\nCluster Hists: \n\n cluster.id variable bin.id lower.bound upper.bound label count\n0 1 AFFINITY_CARD 1 0.0 0.0 0:0 3428\n1 1 AFFINITY_CARD 2 0.0 1.0 0:1 1072\n2 1 AFFINITY_CARD 3 NaN NaN : 0\n3 1 CUST_CREDIT_LIMIT 1 1500.0 1500.0 1500:1500 580\n4 1 CUST_CREDIT_LIMIT 2 1500.0 3000.0 1500:3000 415\n5 1 CUST_CREDIT_LIMIT 3 3000.0 5000.0 3000:5000 405\n6 1 CUST_CREDIT_LIMIT 4 5000.0 7000.0 5000:7000 556\n7 1 CUST_CREDIT_LIMIT 5 7000.0 9000.0 7000:9000 946\n8 1 CUST_CREDIT_LIMIT 6 9000.0 10000.0 9000:10000 373\n9 1 CUST_CREDIT_LIMIT 7 10000.0 11000.0 10000:11000 748\n10 1 CUST_CREDIT_LIMIT 8 11000.0 15000.0 11000:15000 477\n11 1 CUST_CREDIT_LIMIT 9 NaN NaN : 0\n12 1 CUST_GENDER:'Other' 1 NaN NaN : 0\n13 1 CUST_GENDER:F 3 NaN NaN : 1510\n14 1 CUST_GENDER:M 2 NaN NaN : 2990\n15 1 CUST_INCOME_LEVEL:'Other' 1 NaN NaN : 0\n16 1 CUST_INCOME_LEVEL:A: Below 30,000 13 NaN NaN : 88\n17 1 CUST_INCOME_LEVEL:B: 30,000 - 49,999 10 NaN NaN : 222\n18 1 CUST_INCOME_LEVEL:C: 50,000 - 69,999 11 NaN NaN : 171\n19 1 CUST_INCOME_LEVEL:D: 70,000 - 89,999 12 NaN NaN : 133\n20 1 CUST_INCOME_LEVEL:E: 90,000 - 109,999 9 NaN NaN : 310\n21 1 CUST_INCOME_LEVEL:F: 110,000 - 129,999 7 NaN NaN : 369\n22 1 CUST_INCOME_LEVEL:G: 130,000 - 149,999 8 NaN NaN : 339\n23 1 CUST_INCOME_LEVEL:H: 150,000 - 169,999 6 NaN NaN : 383\n24 1 CUST_INCOME_LEVEL:I: 170,000 - 189,999 4 NaN NaN : 484\n25 1 CUST_INCOME_LEVEL:J: 190,000 - 249,999 2 NaN NaN : 965\n26 1 CUST_INCOME_LEVEL:K: 250,000 - 299,999 5 NaN NaN : 393\n27 1 CUST_INCOME_LEVEL:L: 300,000 and above 3 NaN NaN : 643\n28 1 CUST_MARITAL_STATUS:'Other' 1 NaN NaN : 0\n29 1 CUST_MARITAL_STATUS:Divorc. 4 NaN NaN : 615\n30 1 CUST_MARITAL_STATUS:Mabsent 7 NaN NaN : 75\n31 1 CUST_MARITAL_STATUS:Mar-AF 8 NaN NaN : 3\n32 1 CUST_MARITAL_STATUS:Married 2 NaN NaN : 2034\n33 1 CUST_MARITAL_STATUS:NeverM 3 NaN NaN : 1503\n34 1 CUST_MARITAL_STATUS:Separ. 6 NaN NaN : 134\n35 1 CUST_MARITAL_STATUS:Widowed 5 NaN NaN : 136\n36 1 CUST_YEAR_OF_BIRTH 1 1913.0 1920.3 1913:1920.3 5\n37 1 CUST_YEAR_OF_BIRTH 2 1920.3 1927.6 1920.3:1927.6 40\n38 1 CUST_YEAR_OF_BIRTH 3 1927.6 1934.9 1927.6:1934.9 73\n39 1 CUST_YEAR_OF_BIRTH 4 1934.9 1942.2 1934.9:1942.2 192\n40 1 CUST_YEAR_OF_BIRTH 5 1942.2 1949.5 1942.2:1949.5 337\n41 1 CUST_YEAR_OF_BIRTH 6 1949.5 1956.8 1949.5:1956.8 533\n42 1 CUST_YEAR_OF_BIRTH 7 1956.8 1964.1 1956.8:1964.1 878\n43 1 CUST_YEAR_OF_BIRTH 8 1964.1 1971.4 1964.1:1971.4 853\n44 1 CUST_YEAR_OF_BIRTH 9 1971.4 1978.7 1971.4:1978.7 792\n45 1 CUST_YEAR_OF_BIRTH 10 1978.7 1986.0 1978.7:1986 797\n46 1 CUST_YEAR_OF_BIRTH 11 NaN NaN : 0\n47 1 EDUCATION:'Other' 1 NaN NaN : 20\n48 1 EDUCATION:10th 8 NaN NaN : 122\n49 1 EDUCATION:11th 9 NaN NaN : 121\n50 1 EDUCATION:12th 13 NaN NaN : 52\n51 1 EDUCATION:5th-6th 15 NaN NaN : 39\n52 1 EDUCATION:7th-8th 11 NaN NaN : 87\n53 1 EDUCATION:9th 12 NaN NaN : 76\n54 1 EDUCATION:< Bach. 3 NaN NaN : 1041\n55 1 EDUCATION:Assoc-A 7 NaN NaN : 172\n56 1 EDUCATION:Assoc-V 5 NaN NaN : 196\n57 1 EDUCATION:Bach. 4 NaN NaN : 779\n58 1 EDUCATION:HS-grad 2 NaN NaN : 1462\n59 1 EDUCATION:Masters 6 NaN NaN : 190\n60 1 EDUCATION:PhD 14 NaN NaN : 49\n61 1 EDUCATION:Profsc 10 NaN NaN : 94\n62 1 HOUSEHOLD_SIZE:'Other' 1 NaN NaN : 0\n63 1 HOUSEHOLD_SIZE:1 4 NaN NaN : 692\n64 1 HOUSEHOLD_SIZE:2 3 NaN NaN : 1149\n65 1 HOUSEHOLD_SIZE:3 2 NaN NaN : 1787\n66 1 HOUSEHOLD_SIZE:4-5 6 NaN NaN : 219\n67 1 HOUSEHOLD_SIZE:6-8 7 NaN NaN : 148\n68 1 HOUSEHOLD_SIZE:9+ 5 NaN NaN : 505\n69 1 OCCUPATION:'Other' 1 NaN NaN : 2\n70 1 OCCUPATION:? 9 NaN NaN : 236\n71 1 OCCUPATION:Cleric. 6 NaN NaN : 520\n72 1 OCCUPATION:Crafts 2 NaN NaN : 572\n73 1 OCCUPATION:Exec. 4 NaN NaN : 559\n74 1 OCCUPATION:Farming 13 NaN NaN : 122\n75 1 OCCUPATION:Handler 12 NaN NaN : 168\n76 1 OCCUPATION:House-s 15 NaN NaN : 19\n77 1 OCCUPATION:Machine 8 NaN NaN : 276\n78 1 OCCUPATION:Other 7 NaN NaN : 468\n79 1 OCCUPATION:Prof. 5 NaN NaN : 545\n80 1 OCCUPATION:Protec. 14 NaN NaN : 89\n81 1 OCCUPATION:Sales 3 NaN NaN : 560\n82 1 OCCUPATION:TechSup 11 NaN NaN : 177\n83 1 OCCUPATION:Transp. 10 NaN NaN : 187\n84 1 YRS_RESIDENCE 1 0.0 0.0 0:0 48\n85 1 YRS_RESIDENCE 2 0.0 1.0 0:1 276\n86 1 YRS_RESIDENCE 3 1.0 2.0 1:2 625\n87 1 YRS_RESIDENCE 4 2.0 3.0 2:3 961\n88 1 YRS_RESIDENCE 5 3.0 4.0 3:4 937\n89 1 YRS_RESIDENCE 6 4.0 5.0 4:5 757\n90 1 YRS_RESIDENCE 7 5.0 6.0 5:6 473\n91 1 YRS_RESIDENCE 8 6.0 7.0 6:7 231\n92 1 YRS_RESIDENCE 9 7.0 8.0 7:8 101\n93 1 YRS_RESIDENCE 10 8.0 14.0 8:14 91\n94 1 YRS_RESIDENCE 11 NaN NaN : 0\n95 1 Y_BOX_GAMES 1 0.0 0.0 0:0 3094\n96 1 Y_BOX_GAMES 2 0.0 1.0 0:1 1406\n97 1 Y_BOX_GAMES 3 NaN NaN : 0\n98 2 AFFINITY_CARD 1 0.0 0.0 0:0 1145\n99 2 AFFINITY_CARD 2 0.0 1.0 0:1 19\n100 2 AFFINITY_CARD 3 NaN NaN : 0\n101 2 CUST_CREDIT_LIMIT 1 1500.0 1500.0 1500:1500 129\n102 2 CUST_CREDIT_LIMIT 2 1500.0 3000.0 1500:3000 100\n103 2 CUST_CREDIT_LIMIT 3 3000.0 5000.0 3000:5000 93\n104 2 CUST_CREDIT_LIMIT 4 5000.0 7000.0 5000:7000 139\n105 2 CUST_CREDIT_LIMIT 5 7000.0 9000.0 7000:9000 257\n106 2 CUST_CREDIT_LIMIT 6 9000.0 10000.0 9000:10000 82\n107 2 CUST_CREDIT_LIMIT 7 10000.0 11000.0 10000:11000 226\n108 2 CUST_CREDIT_LIMIT 8 11000.0 15000.0 11000:15000 138\n109 2 CUST_CREDIT_LIMIT 9 NaN NaN : 0\n110 2 CUST_GENDER:'Other' 1 NaN NaN : 0\n111 2 CUST_GENDER:F 3 NaN NaN : 553\n112 2 CUST_GENDER:M 2 NaN NaN : 611\n113 2 CUST_INCOME_LEVEL:'Other' 1 NaN NaN : 0\n114 2 CUST_INCOME_LEVEL:A: Below 30,000 13 NaN NaN : 16\n115 2 CUST_INCOME_LEVEL:B: 30,000 - 49,999 10 NaN NaN : 58\n116 2 CUST_INCOME_LEVEL:C: 50,000 - 69,999 11 NaN NaN : 43\n117 2 CUST_INCOME_LEVEL:D: 70,000 - 89,999 12 NaN NaN : 25\n118 2 CUST_INCOME_LEVEL:E: 90,000 - 109,999 9 NaN NaN : 52\n119 2 CUST_INCOME_LEVEL:F: 110,000 - 129,999 7 NaN NaN : 95\n120 2 CUST_INCOME_LEVEL:G: 130,000 - 149,999 8 NaN NaN : 95\n121 2 CUST_INCOME_LEVEL:H: 150,000 - 169,999 6 NaN NaN : 75\n122 2 CUST_INCOME_LEVEL:I: 170,000 - 189,999 4 NaN NaN : 96\n123 2 CUST_INCOME_LEVEL:J: 190,000 - 249,999 2 NaN NaN : 266\n124 2 CUST_INCOME_LEVEL:K: 250,000 - 299,999 5 NaN NaN : 126\n125 2 CUST_INCOME_LEVEL:L: 300,000 and above 3 NaN NaN : 217\n126 2 CUST_MARITAL_STATUS:'Other' 1 NaN NaN : 0\n127 2 CUST_MARITAL_STATUS:Divorc. 4 NaN NaN : 61\n128 2 CUST_MARITAL_STATUS:Mabsent 7 NaN NaN : 9\n129 2 CUST_MARITAL_STATUS:Mar-AF 8 NaN NaN : 0\n130 2 CUST_MARITAL_STATUS:Married 2 NaN NaN : 25\n131 2 CUST_MARITAL_STATUS:NeverM 3 NaN NaN : 1041\n132 2 CUST_MARITAL_STATUS:Separ. 6 NaN NaN : 28\n133 2 CUST_MARITAL_STATUS:Widowed 5 NaN NaN : 0\n134 2 CUST_YEAR_OF_BIRTH 1 1913.0 1920.3 1913:1920.3 0\n135 2 CUST_YEAR_OF_BIRTH 2 1920.3 1927.6 1920.3:1927.6 0\n136 2 CUST_YEAR_OF_BIRTH 3 1927.6 1934.9 1927.6:1934.9 0\n137 2 CUST_YEAR_OF_BIRTH 4 1934.9 1942.2 1934.9:1942.2 0\n138 2 CUST_YEAR_OF_BIRTH 5 1942.2 1949.5 1942.2:1949.5 0\n139 2 CUST_YEAR_OF_BIRTH 6 1949.5 1956.8 1949.5:1956.8 0\n140 2 CUST_YEAR_OF_BIRTH 7 1956.8 1964.1 1956.8:1964.1 0\n141 2 CUST_YEAR_OF_BIRTH 8 1964.1 1971.4 1964.1:1971.4 0\n142 2 CUST_YEAR_OF_BIRTH 9 1971.4 1978.7 1971.4:1978.7 404\n143 2 CUST_YEAR_OF_BIRTH 10 1978.7 1986.0 1978.7:1986 760\n144 2 CUST_YEAR_OF_BIRTH 11 NaN NaN : 0\n145 2 EDUCATION:'Other' 1 NaN NaN : 3\n146 2 EDUCATION:10th 8 NaN NaN : 60\n147 2 EDUCATION:11th 9 NaN NaN : 54\n148 2 EDUCATION:12th 13 NaN NaN : 26\n149 2 EDUCATION:5th-6th 15 NaN NaN : 10\n150 2 EDUCATION:7th-8th 11 NaN NaN : 7\n151 2 EDUCATION:9th 12 NaN NaN : 8\n152 2 EDUCATION:< Bach. 3 NaN NaN : 380\n153 2 EDUCATION:Assoc-A 7 NaN NaN : 37\n154 2 EDUCATION:Assoc-V 5 NaN NaN : 42\n155 2 EDUCATION:Bach. 4 NaN NaN : 162\n156 2 EDUCATION:HS-grad 2 NaN NaN : 352\n157 2 EDUCATION:Masters 6 NaN NaN : 6\n158 2 EDUCATION:PhD 14 NaN NaN : 3\n159 2 EDUCATION:Profsc 10 NaN NaN : 14\n160 2 HOUSEHOLD_SIZE:'Other' 1 NaN NaN : 0\n161 2 HOUSEHOLD_SIZE:1 4 NaN NaN : 580\n162 2 HOUSEHOLD_SIZE:2 3 NaN NaN : 384\n163 2 HOUSEHOLD_SIZE:3 2 NaN NaN : 0\n164 2 HOUSEHOLD_SIZE:4-5 6 NaN NaN : 13\n165 2 HOUSEHOLD_SIZE:6-8 7 NaN NaN : 88\n166 2 HOUSEHOLD_SIZE:9+ 5 NaN NaN : 99\n167 2 OCCUPATION:'Other' 1 NaN NaN : 1\n168 2 OCCUPATION:? 9 NaN NaN : 112\n169 2 OCCUPATION:Cleric. 6 NaN NaN : 186\n170 2 OCCUPATION:Crafts 2 NaN NaN : 88\n171 2 OCCUPATION:Exec. 4 NaN NaN : 58\n172 2 OCCUPATION:Farming 13 NaN NaN : 27\n173 2 OCCUPATION:Handler 12 NaN NaN : 69\n174 2 OCCUPATION:House-s 15 NaN NaN : 3\n175 2 OCCUPATION:Machine 8 NaN NaN : 55\n176 2 OCCUPATION:Other 7 NaN NaN : 218\n177 2 OCCUPATION:Prof. 5 NaN NaN : 94\n178 2 OCCUPATION:Protec. 14 NaN NaN : 12\n179 2 OCCUPATION:Sales 3 NaN NaN : 165\n180 2 OCCUPATION:TechSup 11 NaN NaN : 54\n181 2 OCCUPATION:Transp. 10 NaN NaN : 22\n182 2 YRS_RESIDENCE 1 0.0 0.0 0:0 33\n183 2 YRS_RESIDENCE 2 0.0 1.0 0:1 250\n184 2 YRS_RESIDENCE 3 1.0 2.0 1:2 477\n185 2 YRS_RESIDENCE 4 2.0 3.0 2:3 347\n186 2 YRS_RESIDENCE 5 3.0 4.0 3:4 46\n187 2 YRS_RESIDENCE 6 4.0 5.0 4:5 10\n188 2 YRS_RESIDENCE 7 5.0 6.0 5:6 1\n189 2 YRS_RESIDENCE 8 6.0 7.0 6:7 0\n190 2 YRS_RESIDENCE 9 7.0 8.0 7:8 0\n191 2 YRS_RESIDENCE 10 8.0 14.0 8:14 0\n192 2 YRS_RESIDENCE 11 NaN NaN : 0\n193 2 Y_BOX_GAMES 1 0.0 0.0 0:0 0\n194 2 Y_BOX_GAMES 2 0.0 1.0 0:1 1164\n195 2 Y_BOX_GAMES 3 NaN NaN : 0\n196 3 AFFINITY_CARD 1 0.0 0.0 0:0 2283\n197 3 AFFINITY_CARD 2 0.0 1.0 0:1 1053\n198 3 AFFINITY_CARD 3 NaN NaN : 0\n199 3 CUST_CREDIT_LIMIT 1 1500.0 1500.0 1500:1500 451\n200 3 CUST_CREDIT_LIMIT 2 1500.0 3000.0 1500:3000 315\n201 3 CUST_CREDIT_LIMIT 3 3000.0 5000.0 3000:5000 312\n202 3 CUST_CREDIT_LIMIT 4 5000.0 7000.0 5000:7000 417\n203 3 CUST_CREDIT_LIMIT 5 7000.0 9000.0 7000:9000 689\n204 3 CUST_CREDIT_LIMIT 6 9000.0 10000.0 9000:10000 291\n205 3 CUST_CREDIT_LIMIT 7 10000.0 11000.0 10000:11000 522\n206 3 CUST_CREDIT_LIMIT 8 11000.0 15000.0 11000:15000 339\n207 3 CUST_CREDIT_LIMIT 9 NaN NaN : 0\n208 3 CUST_GENDER:'Other' 1 NaN NaN : 0\n209 3 CUST_GENDER:F 3 NaN NaN : 957\n210 3 CUST_GENDER:M 2 NaN NaN : 2379\n211 3 CUST_INCOME_LEVEL:'Other' 1 NaN NaN : 0\n212 3 CUST_INCOME_LEVEL:A: Below 30,000 13 NaN NaN : 72\n213 3 CUST_INCOME_LEVEL:B: 30,000 - 49,999 10 NaN NaN : 164\n214 3 CUST_INCOME_LEVEL:C: 50,000 - 69,999 11 NaN NaN : 128\n215 3 CUST_INCOME_LEVEL:D: 70,000 - 89,999 12 NaN NaN : 108\n216 3 CUST_INCOME_LEVEL:E: 90,000 - 109,999 9 NaN NaN : 258\n217 3 CUST_INCOME_LEVEL:F: 110,000 - 129,999 7 NaN NaN : 274\n218 3 CUST_INCOME_LEVEL:G: 130,000 - 149,999 8 NaN NaN : 244\n219 3 CUST_INCOME_LEVEL:H: 150,000 - 169,999 6 NaN NaN : 308\n220 3 CUST_INCOME_LEVEL:I: 170,000 - 189,999 4 NaN NaN : 388\n221 3 CUST_INCOME_LEVEL:J: 190,000 - 249,999 2 NaN NaN : 699\n222 3 CUST_INCOME_LEVEL:K: 250,000 - 299,999 5 NaN NaN : 267\n223 3 CUST_INCOME_LEVEL:L: 300,000 and above 3 NaN NaN : 426\n224 3 CUST_MARITAL_STATUS:'Other' 1 NaN NaN : 0\n225 3 CUST_MARITAL_STATUS:Divorc. 4 NaN NaN : 554\n226 3 CUST_MARITAL_STATUS:Mabsent 7 NaN NaN : 66\n227 3 CUST_MARITAL_STATUS:Mar-AF 8 NaN NaN : 3\n228 3 CUST_MARITAL_STATUS:Married 2 NaN NaN : 2009\n229 3 CUST_MARITAL_STATUS:NeverM 3 NaN NaN : 462\n230 3 CUST_MARITAL_STATUS:Separ. 6 NaN NaN : 106\n231 3 CUST_MARITAL_STATUS:Widowed 5 NaN NaN : 136\n232 3 CUST_YEAR_OF_BIRTH 1 1913.0 1920.3 1913:1920.3 5\n233 3 CUST_YEAR_OF_BIRTH 2 1920.3 1927.6 1920.3:1927.6 40\n234 3 CUST_YEAR_OF_BIRTH 3 1927.6 1934.9 1927.6:1934.9 73\n235 3 CUST_YEAR_OF_BIRTH 4 1934.9 1942.2 1934.9:1942.2 192\n236 3 CUST_YEAR_OF_BIRTH 5 1942.2 1949.5 1942.2:1949.5 337\n237 3 CUST_YEAR_OF_BIRTH 6 1949.5 1956.8 1949.5:1956.8 533\n238 3 CUST_YEAR_OF_BIRTH 7 1956.8 1964.1 1956.8:1964.1 878\n239 3 CUST_YEAR_OF_BIRTH 8 1964.1 1971.4 1964.1:1971.4 853\n240 3 CUST_YEAR_OF_BIRTH 9 1971.4 1978.7 1971.4:1978.7 388\n241 3 CUST_YEAR_OF_BIRTH 10 1978.7 1986.0 1978.7:1986 37\n242 3 CUST_YEAR_OF_BIRTH 11 NaN NaN : 0\n243 3 EDUCATION:'Other' 1 NaN NaN : 17\n244 3 EDUCATION:10th 8 NaN NaN : 62\n245 3 EDUCATION:11th 9 NaN NaN : 67\n246 3 EDUCATION:12th 13 NaN NaN : 26\n247 3 EDUCATION:5th-6th 15 NaN NaN : 29\n248 3 EDUCATION:7th-8th 11 NaN NaN : 80\n249 3 EDUCATION:9th 12 NaN NaN : 68\n250 3 EDUCATION:< Bach. 3 NaN NaN : 661\n251 3 EDUCATION:Assoc-A 7 NaN NaN : 135\n252 3 EDUCATION:Assoc-V 5 NaN NaN : 154\n253 3 EDUCATION:Bach. 4 NaN NaN : 617\n254 3 EDUCATION:HS-grad 2 NaN NaN : 1110\n255 3 EDUCATION:Masters 6 NaN NaN : 184\n256 3 EDUCATION:PhD 14 NaN NaN : 46\n257 3 EDUCATION:Profsc 10 NaN NaN : 80\n258 3 HOUSEHOLD_SIZE:'Other' 1 NaN NaN : 0\n259 3 HOUSEHOLD_SIZE:1 4 NaN NaN : 112\n260 3 HOUSEHOLD_SIZE:2 3 NaN NaN : 765\n261 3 HOUSEHOLD_SIZE:3 2 NaN NaN : 1787\n262 3 HOUSEHOLD_SIZE:4-5 6 NaN NaN : 206\n263 3 HOUSEHOLD_SIZE:6-8 7 NaN NaN : 60\n264 3 HOUSEHOLD_SIZE:9+ 5 NaN NaN : 406\n265 3 OCCUPATION:'Other' 1 NaN NaN : 1\n266 3 OCCUPATION:? 9 NaN NaN : 124\n267 3 OCCUPATION:Cleric. 6 NaN NaN : 334\n268 3 OCCUPATION:Crafts 2 NaN NaN : 484\n269 3 OCCUPATION:Exec. 4 NaN NaN : 501\n270 3 OCCUPATION:Farming 13 NaN NaN : 95\n271 3 OCCUPATION:Handler 12 NaN NaN : 99\n272 3 OCCUPATION:House-s 15 NaN NaN : 16\n273 3 OCCUPATION:Machine 8 NaN NaN : 221\n274 3 OCCUPATION:Other 7 NaN NaN : 250\n275 3 OCCUPATION:Prof. 5 NaN NaN : 451\n276 3 OCCUPATION:Protec. 14 NaN NaN : 77\n277 3 OCCUPATION:Sales 3 NaN NaN : 395\n278 3 OCCUPATION:TechSup 11 NaN NaN : 123\n279 3 OCCUPATION:Transp. 10 NaN NaN : 165\n280 3 YRS_RESIDENCE 1 0.0 0.0 0:0 15\n281 3 YRS_RESIDENCE 2 0.0 1.0 0:1 26\n282 3 YRS_RESIDENCE 3 1.0 2.0 1:2 148\n283 3 YRS_RESIDENCE 4 2.0 3.0 2:3 614\n284 3 YRS_RESIDENCE 5 3.0 4.0 3:4 891\n285 3 YRS_RESIDENCE 6 4.0 5.0 4:5 747\n286 3 YRS_RESIDENCE 7 5.0 6.0 5:6 472\n287 3 YRS_RESIDENCE 8 6.0 7.0 6:7 231\n288 3 YRS_RESIDENCE 9 7.0 8.0 7:8 101\n289 3 YRS_RESIDENCE 10 8.0 14.0 8:14 91\n290 3 YRS_RESIDENCE 11 NaN NaN : 0\n291 3 Y_BOX_GAMES 1 0.0 0.0 0:0 3094\n292 3 Y_BOX_GAMES 2 0.0 1.0 0:1 242\n293 3 Y_BOX_GAMES 3 NaN NaN : 0\n294 4 AFFINITY_CARD 1 0.0 0.0 0:0 2203\n295 4 AFFINITY_CARD 2 0.0 1.0 0:1 952\n296 4 AFFINITY_CARD 3 NaN NaN : 0\n297 4 CUST_CREDIT_LIMIT 1 1500.0 1500.0 1500:1500 426\n298 4 CUST_CREDIT_LIMIT 2 1500.0 3000.0 1500:3000 300\n299 4 CUST_CREDIT_LIMIT 3 3000.0 5000.0 3000:5000 294\n300 4 CUST_CREDIT_LIMIT 4 5000.0 7000.0 5000:7000 391\n301 4 CUST_CREDIT_LIMIT 5 7000.0 9000.0 7000:9000 654\n302 4 CUST_CREDIT_LIMIT 6 9000.0 10000.0 9000:10000 278\n303 4 CUST_CREDIT_LIMIT 7 10000.0 11000.0 10000:11000 496\n304 4 CUST_CREDIT_LIMIT 8 11000.0 15000.0 11000:15000 316\n305 4 CUST_CREDIT_LIMIT 9 NaN NaN : 0\n306 4 CUST_GENDER:'Other' 1 NaN NaN : 0\n307 4 CUST_GENDER:F 3 NaN NaN : 776\n308 4 CUST_GENDER:M 2 NaN NaN : 2379\n309 4 CUST_INCOME_LEVEL:'Other' 1 NaN NaN : 0\n310 4 CUST_INCOME_LEVEL:A: Below 30,000 13 NaN NaN : 70\n311 4 CUST_INCOME_LEVEL:B: 30,000 - 49,999 10 NaN NaN : 159\n312 4 CUST_INCOME_LEVEL:C: 50,000 - 69,999 11 NaN NaN : 124\n313 4 CUST_INCOME_LEVEL:D: 70,000 - 89,999 12 NaN NaN : 104\n314 4 CUST_INCOME_LEVEL:E: 90,000 - 109,999 9 NaN NaN : 233\n315 4 CUST_INCOME_LEVEL:F: 110,000 - 129,999 7 NaN NaN : 260\n316 4 CUST_INCOME_LEVEL:G: 130,000 - 149,999 8 NaN NaN : 236\n317 4 CUST_INCOME_LEVEL:H: 150,000 - 169,999 6 NaN NaN : 293\n318 4 CUST_INCOME_LEVEL:I: 170,000 - 189,999 4 NaN NaN : 361\n319 4 CUST_INCOME_LEVEL:J: 190,000 - 249,999 2 NaN NaN : 658\n320 4 CUST_INCOME_LEVEL:K: 250,000 - 299,999 5 NaN NaN : 250\n321 4 CUST_INCOME_LEVEL:L: 300,000 and above 3 NaN NaN : 407\n322 4 CUST_MARITAL_STATUS:'Other' 1 NaN NaN : 0\n323 4 CUST_MARITAL_STATUS:Divorc. 4 NaN NaN : 554\n324 4 CUST_MARITAL_STATUS:Mabsent 7 NaN NaN : 66\n325 4 CUST_MARITAL_STATUS:Mar-AF 8 NaN NaN : 1\n326 4 CUST_MARITAL_STATUS:Married 2 NaN NaN : 1830\n327 4 CUST_MARITAL_STATUS:NeverM 3 NaN NaN : 462\n328 4 CUST_MARITAL_STATUS:Separ. 6 NaN NaN : 106\n329 4 CUST_MARITAL_STATUS:Widowed 5 NaN NaN : 136\n330 4 CUST_YEAR_OF_BIRTH 1 1913.0 1920.3 1913:1920.3 5\n331 4 CUST_YEAR_OF_BIRTH 2 1920.3 1927.6 1920.3:1927.6 40\n332 4 CUST_YEAR_OF_BIRTH 3 1927.6 1934.9 1927.6:1934.9 73\n333 4 CUST_YEAR_OF_BIRTH 4 1934.9 1942.2 1934.9:1942.2 188\n334 4 CUST_YEAR_OF_BIRTH 5 1942.2 1949.5 1942.2:1949.5 325\n335 4 CUST_YEAR_OF_BIRTH 6 1949.5 1956.8 1949.5:1956.8 507\n336 4 CUST_YEAR_OF_BIRTH 7 1956.8 1964.1 1956.8:1964.1 821\n337 4 CUST_YEAR_OF_BIRTH 8 1964.1 1971.4 1964.1:1971.4 778\n338 4 CUST_YEAR_OF_BIRTH 9 1971.4 1978.7 1971.4:1978.7 381\n339 4 CUST_YEAR_OF_BIRTH 10 1978.7 1986.0 1978.7:1986 37\n340 4 CUST_YEAR_OF_BIRTH 11 NaN NaN : 0\n341 4 EDUCATION:'Other' 1 NaN NaN : 16\n342 4 EDUCATION:10th 8 NaN NaN : 59\n343 4 EDUCATION:11th 9 NaN NaN : 63\n344 4 EDUCATION:12th 13 NaN NaN : 25\n345 4 EDUCATION:5th-6th 15 NaN NaN : 29\n346 4 EDUCATION:7th-8th 11 NaN NaN : 78\n347 4 EDUCATION:9th 12 NaN NaN : 63\n348 4 EDUCATION:< Bach. 3 NaN NaN : 628\n349 4 EDUCATION:Assoc-A 7 NaN NaN : 127\n350 4 EDUCATION:Assoc-V 5 NaN NaN : 144\n351 4 EDUCATION:Bach. 4 NaN NaN : 581\n352 4 EDUCATION:HS-grad 2 NaN NaN : 1051\n353 4 EDUCATION:Masters 6 NaN NaN : 171\n354 4 EDUCATION:PhD 14 NaN NaN : 44\n355 4 EDUCATION:Profsc 10 NaN NaN : 76\n356 4 HOUSEHOLD_SIZE:'Other' 1 NaN NaN : 0\n357 4 HOUSEHOLD_SIZE:1 4 NaN NaN : 110\n358 4 HOUSEHOLD_SIZE:2 3 NaN NaN : 765\n359 4 HOUSEHOLD_SIZE:3 2 NaN NaN : 1787\n360 4 HOUSEHOLD_SIZE:4-5 6 NaN NaN : 27\n361 4 HOUSEHOLD_SIZE:6-8 7 NaN NaN : 60\n362 4 HOUSEHOLD_SIZE:9+ 5 NaN NaN : 406\n363 4 OCCUPATION:'Other' 1 NaN NaN : 1\n364 4 OCCUPATION:? 9 NaN NaN : 111\n365 4 OCCUPATION:Cleric. 6 NaN NaN : 291\n366 4 OCCUPATION:Crafts 2 NaN NaN : 479\n367 4 OCCUPATION:Exec. 4 NaN NaN : 466\n368 4 OCCUPATION:Farming 13 NaN NaN : 92\n369 4 OCCUPATION:Handler 12 NaN NaN : 96\n370 4 OCCUPATION:House-s 15 NaN NaN : 16\n371 4 OCCUPATION:Machine 8 NaN NaN : 211\n372 4 OCCUPATION:Other 7 NaN NaN : 234\n373 4 OCCUPATION:Prof. 5 NaN NaN : 421\n374 4 OCCUPATION:Protec. 14 NaN NaN : 77\n375 4 OCCUPATION:Sales 3 NaN NaN : 383\n376 4 OCCUPATION:TechSup 11 NaN NaN : 116\n377 4 OCCUPATION:Transp. 10 NaN NaN : 161\n378 4 YRS_RESIDENCE 1 0.0 0.0 0:0 13\n379 4 YRS_RESIDENCE 2 0.0 1.0 0:1 22\n380 4 YRS_RESIDENCE 3 1.0 2.0 1:2 132\n381 4 YRS_RESIDENCE 4 2.0 3.0 2:3 570\n382 4 YRS_RESIDENCE 5 3.0 4.0 3:4 833\n383 4 YRS_RESIDENCE 6 4.0 5.0 4:5 708\n384 4 YRS_RESIDENCE 7 5.0 6.0 5:6 464\n385 4 YRS_RESIDENCE 8 6.0 7.0 6:7 226\n386 4 YRS_RESIDENCE 9 7.0 8.0 7:8 97\n387 4 YRS_RESIDENCE 10 8.0 14.0 8:14 90\n388 4 YRS_RESIDENCE 11 NaN NaN : 0\n389 4 Y_BOX_GAMES 1 0.0 0.0 0:0 2913\n390 4 Y_BOX_GAMES 2 0.0 1.0 0:1 242\n391 4 Y_BOX_GAMES 3 NaN NaN : 0\n392 5 AFFINITY_CARD 1 0.0 0.0 0:0 80\n393 5 AFFINITY_CARD 2 0.0 1.0 0:1 101\n394 5 AFFINITY_CARD 3 NaN NaN : 0\n395 5 CUST_CREDIT_LIMIT 1 1500.0 1500.0 1500:1500 25\n396 5 CUST_CREDIT_LIMIT 2 1500.0 3000.0 1500:3000 15\n397 5 CUST_CREDIT_LIMIT 3 3000.0 5000.0 3000:5000 18\n398 5 CUST_CREDIT_LIMIT 4 5000.0 7000.0 5000:7000 26\n399 5 CUST_CREDIT_LIMIT 5 7000.0 9000.0 7000:9000 35\n400 5 CUST_CREDIT_LIMIT 6 9000.0 10000.0 9000:10000 13\n401 5 CUST_CREDIT_LIMIT 7 10000.0 11000.0 10000:11000 26\n402 5 CUST_CREDIT_LIMIT 8 11000.0 15000.0 11000:15000 23\n403 5 CUST_CREDIT_LIMIT 9 NaN NaN : 0\n404 5 CUST_GENDER:'Other' 1 NaN NaN : 0\n405 5 CUST_GENDER:F 3 NaN NaN : 181\n406 5 CUST_GENDER:M 2 NaN NaN : 0\n407 5 CUST_INCOME_LEVEL:'Other' 1 NaN NaN : 0\n408 5 CUST_INCOME_LEVEL:A: Below 30,000 13 NaN NaN : 2\n409 5 CUST_INCOME_LEVEL:B: 30,000 - 49,999 10 NaN NaN : 5\n410 5 CUST_INCOME_LEVEL:C: 50,000 - 69,999 11 NaN NaN : 4\n411 5 CUST_INCOME_LEVEL:D: 70,000 - 89,999 12 NaN NaN : 4\n412 5 CUST_INCOME_LEVEL:E: 90,000 - 109,999 9 NaN NaN : 25\n413 5 CUST_INCOME_LEVEL:F: 110,000 - 129,999 7 NaN NaN : 14\n414 5 CUST_INCOME_LEVEL:G: 130,000 - 149,999 8 NaN NaN : 8\n415 5 CUST_INCOME_LEVEL:H: 150,000 - 169,999 6 NaN NaN : 15\n416 5 CUST_INCOME_LEVEL:I: 170,000 - 189,999 4 NaN NaN : 27\n417 5 CUST_INCOME_LEVEL:J: 190,000 - 249,999 2 NaN NaN : 41\n418 5 CUST_INCOME_LEVEL:K: 250,000 - 299,999 5 NaN NaN : 17\n419 5 CUST_INCOME_LEVEL:L: 300,000 and above 3 NaN NaN : 19\n420 5 CUST_MARITAL_STATUS:'Other' 1 NaN NaN : 0\n421 5 CUST_MARITAL_STATUS:Divorc. 4 NaN NaN : 0\n422 5 CUST_MARITAL_STATUS:Mabsent 7 NaN NaN : 0\n423 5 CUST_MARITAL_STATUS:Mar-AF 8 NaN NaN : 2\n424 5 CUST_MARITAL_STATUS:Married 2 NaN NaN : 179\n425 5 CUST_MARITAL_STATUS:NeverM 3 NaN NaN : 0\n426 5 CUST_MARITAL_STATUS:Separ. 6 NaN NaN : 0\n427 5 CUST_MARITAL_STATUS:Widowed 5 NaN NaN : 0\n428 5 CUST_YEAR_OF_BIRTH 1 1913.0 1920.3 1913:1920.3 0\n429 5 CUST_YEAR_OF_BIRTH 2 1920.3 1927.6 1920.3:1927.6 0\n430 5 CUST_YEAR_OF_BIRTH 3 1927.6 1934.9 1927.6:1934.9 0\n431 5 CUST_YEAR_OF_BIRTH 4 1934.9 1942.2 1934.9:1942.2 4\n432 5 CUST_YEAR_OF_BIRTH 5 1942.2 1949.5 1942.2:1949.5 12\n433 5 CUST_YEAR_OF_BIRTH 6 1949.5 1956.8 1949.5:1956.8 26\n434 5 CUST_YEAR_OF_BIRTH 7 1956.8 1964.1 1956.8:1964.1 57\n435 5 CUST_YEAR_OF_BIRTH 8 1964.1 1971.4 1964.1:1971.4 75\n436 5 CUST_YEAR_OF_BIRTH 9 1971.4 1978.7 1971.4:1978.7 7\n437 5 CUST_YEAR_OF_BIRTH 10 1978.7 1986.0 1978.7:1986 0\n438 5 CUST_YEAR_OF_BIRTH 11 NaN NaN : 0\n439 5 EDUCATION:'Other' 1 NaN NaN : 1\n440 5 EDUCATION:10th 8 NaN NaN : 3\n441 5 EDUCATION:11th 9 NaN NaN : 4\n442 5 EDUCATION:12th 13 NaN NaN : 1\n443 5 EDUCATION:5th-6th 15 NaN NaN : 0\n444 5 EDUCATION:7th-8th 11 NaN NaN : 2\n445 5 EDUCATION:9th 12 NaN NaN : 5\n446 5 EDUCATION:< Bach. 3 NaN NaN : 33\n447 5 EDUCATION:Assoc-A 7 NaN NaN : 8\n448 5 EDUCATION:Assoc-V 5 NaN NaN : 10\n449 5 EDUCATION:Bach. 4 NaN NaN : 36\n450 5 EDUCATION:HS-grad 2 NaN NaN : 59\n451 5 EDUCATION:Masters 6 NaN NaN : 13\n452 5 EDUCATION:PhD 14 NaN NaN : 2\n453 5 EDUCATION:Profsc 10 NaN NaN : 4\n454 5 HOUSEHOLD_SIZE:'Other' 1 NaN NaN : 0\n455 5 HOUSEHOLD_SIZE:1 4 NaN NaN : 2\n456 5 HOUSEHOLD_SIZE:2 3 NaN NaN : 0\n457 5 HOUSEHOLD_SIZE:3 2 NaN NaN : 0\n458 5 HOUSEHOLD_SIZE:4-5 6 NaN NaN : 179\n459 5 HOUSEHOLD_SIZE:6-8 7 NaN NaN : 0\n460 5 HOUSEHOLD_SIZE:9+ 5 NaN NaN : 0\n461 5 OCCUPATION:'Other' 1 NaN NaN : 0\n462 5 OCCUPATION:? 9 NaN NaN : 13\n463 5 OCCUPATION:Cleric. 6 NaN NaN : 43\n464 5 OCCUPATION:Crafts 2 NaN NaN : 5\n465 5 OCCUPATION:Exec. 4 NaN NaN : 35\n466 5 OCCUPATION:Farming 13 NaN NaN : 3\n467 5 OCCUPATION:Handler 12 NaN NaN : 3\n468 5 OCCUPATION:House-s 15 NaN NaN : 0\n469 5 OCCUPATION:Machine 8 NaN NaN : 10\n470 5 OCCUPATION:Other 7 NaN NaN : 16\n471 5 OCCUPATION:Prof. 5 NaN NaN : 30\n472 5 OCCUPATION:Protec. 14 NaN NaN : 0\n473 5 OCCUPATION:Sales 3 NaN NaN : 12\n474 5 OCCUPATION:TechSup 11 NaN NaN : 7\n475 5 OCCUPATION:Transp. 10 NaN NaN : 4\n476 5 YRS_RESIDENCE 1 0.0 0.0 0:0 2\n477 5 YRS_RESIDENCE 2 0.0 1.0 0:1 4\n478 5 YRS_RESIDENCE 3 1.0 2.0 1:2 16\n479 5 YRS_RESIDENCE 4 2.0 3.0 2:3 44\n480 5 YRS_RESIDENCE 5 3.0 4.0 3:4 58\n481 5 YRS_RESIDENCE 6 4.0 5.0 4:5 39\n482 5 YRS_RESIDENCE 7 5.0 6.0 5:6 8\n483 5 YRS_RESIDENCE 8 6.0 7.0 6:7 5\n484 5 YRS_RESIDENCE 9 7.0 8.0 7:8 4\n485 5 YRS_RESIDENCE 10 8.0 14.0 8:14 1\n486 5 YRS_RESIDENCE 11 NaN NaN : 0\n487 5 Y_BOX_GAMES 1 0.0 0.0 0:0 181\n488 5 Y_BOX_GAMES 2 0.0 1.0 0:1 0\n489 5 Y_BOX_GAMES 3 NaN NaN : 0\n\nRules: \n\n cluster.id rhs.support rhs.conf lhr.support lhs.conf lhs.var lhs.var.support lhs.var.conf predicate\n0 1 4500 1.000000 3428 0.761778 AFFINITY_CARD 3428 0.500000 AFFINITY_CARD <= 0\n1 1 4500 1.000000 3428 0.761778 AFFINITY_CARD 3428 0.500000 AFFINITY_CARD >= 0\n2 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN < Bach.\n3 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN Assoc-A\n4 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN Assoc-V\n5 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN Bach.\n6 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN HS-grad\n7 1 4500 1.000000 3840 0.761778 EDUCATION 3428 0.600000 EDUCATION IN Masters\n8 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n9 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n10 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n11 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n12 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n13 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n14 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n15 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n16 1 4500 1.000000 4108 0.761778 CUST_INCOME_LEVEL 3428 0.250000 CUST_INCOME_LEVEL IN L: 300,000 and above\n17 1 4500 1.000000 4133 0.761778 HOUSEHOLD_SIZE 3428 0.333333 HOUSEHOLD_SIZE IN 1\n18 1 4500 1.000000 4133 0.761778 HOUSEHOLD_SIZE 3428 0.333333 HOUSEHOLD_SIZE IN 2\n19 1 4500 1.000000 4133 0.761778 HOUSEHOLD_SIZE 3428 0.333333 HOUSEHOLD_SIZE IN 3\n20 1 4500 1.000000 4133 0.761778 HOUSEHOLD_SIZE 3428 0.333333 HOUSEHOLD_SIZE IN 9+\n21 1 4500 1.000000 4152 0.761778 CUST_MARITAL_STATUS 3428 0.571429 CUST_MARITAL_STATUS IN Divorc.\n22 1 4500 1.000000 4152 0.761778 CUST_MARITAL_STATUS 3428 0.571429 CUST_MARITAL_STATUS IN Married\n23 1 4500 1.000000 4152 0.761778 CUST_MARITAL_STATUS 3428 0.571429 CUST_MARITAL_STATUS IN NeverM\n24 1 4500 1.000000 4190 0.761778 CUST_YEAR_OF_BIRTH 3428 0.400000 CUST_YEAR_OF_BIRTH <= 1986\n25 1 4500 1.000000 4190 0.761778 CUST_YEAR_OF_BIRTH 3428 0.400000 CUST_YEAR_OF_BIRTH > 1942.2\n26 1 4500 1.000000 4260 0.761778 YRS_RESIDENCE 3428 0.300000 YRS_RESIDENCE <= 7\n27 1 4500 1.000000 4260 0.761778 YRS_RESIDENCE 3428 0.300000 YRS_RESIDENCE > 0\n28 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN ?\n29 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Cleric.\n30 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Crafts\n31 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Exec.\n32 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Handler\n33 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Machine\n34 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Other\n35 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Prof.\n36 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Sales\n37 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN TechSup\n38 1 4500 1.000000 4268 0.761778 OCCUPATION 3428 0.266667 OCCUPATION IN Transp.\n39 1 4500 1.000000 4500 0.761778 CUST_CREDIT_LIMIT 3428 0.000000 CUST_CREDIT_LIMIT <= 15000\n40 1 4500 1.000000 4500 0.761778 CUST_CREDIT_LIMIT 3428 0.000000 CUST_CREDIT_LIMIT >= 1500\n41 1 4500 1.000000 4500 0.761778 CUST_GENDER 3428 0.000000 CUST_GENDER IN F\n42 1 4500 1.000000 4500 0.761778 CUST_GENDER 3428 0.000000 CUST_GENDER IN M\n43 1 4500 1.000000 4500 0.761778 Y_BOX_GAMES 3428 0.000000 Y_BOX_GAMES <= 1\n44 1 4500 1.000000 4500 0.761778 Y_BOX_GAMES 3428 0.000000 Y_BOX_GAMES >= 0\n45 2 1164 0.258667 1041 0.894330 CUST_MARITAL_STATUS 1041 0.106960 CUST_MARITAL_STATUS = NeverM\n46 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN 10th\n47 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN 11th\n48 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN < Bach.\n49 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN Assoc-V\n50 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN Bach.\n51 2 1164 0.258667 1050 0.894330 EDUCATION 1041 0.008475 EDUCATION IN HS-grad\n52 2 1164 0.258667 1063 0.894330 HOUSEHOLD_SIZE 1041 0.111346 HOUSEHOLD_SIZE IN 1\n53 2 1164 0.258667 1063 0.894330 HOUSEHOLD_SIZE 1041 0.111346 HOUSEHOLD_SIZE IN 2\n54 2 1164 0.258667 1063 0.894330 HOUSEHOLD_SIZE 1041 0.111346 HOUSEHOLD_SIZE IN 9+\n55 2 1164 0.258667 1074 0.894330 YRS_RESIDENCE 1041 0.089257 YRS_RESIDENCE <= 3\n56 2 1164 0.258667 1074 0.894330 YRS_RESIDENCE 1041 0.089257 YRS_RESIDENCE > 0\n57 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n58 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n59 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n60 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n61 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n62 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n63 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n64 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n65 2 1164 0.258667 1080 0.894330 CUST_INCOME_LEVEL 1041 0.002256 CUST_INCOME_LEVEL IN L: 300,000 and above\n66 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN ?\n67 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Cleric.\n68 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Crafts\n69 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Exec.\n70 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Handler\n71 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Machine\n72 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Other\n73 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Prof.\n74 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN Sales\n75 2 1164 0.258667 1099 0.894330 OCCUPATION 1041 0.011051 OCCUPATION IN TechSup\n76 2 1164 0.258667 1145 0.894330 AFFINITY_CARD 1041 0.093743 AFFINITY_CARD <= 0\n77 2 1164 0.258667 1145 0.894330 AFFINITY_CARD 1041 0.093743 AFFINITY_CARD >= 0\n78 2 1164 0.258667 1164 0.894330 CUST_CREDIT_LIMIT 1041 0.000769 CUST_CREDIT_LIMIT <= 15000\n79 2 1164 0.258667 1164 0.894330 CUST_CREDIT_LIMIT 1041 0.000769 CUST_CREDIT_LIMIT >= 1500\n80 2 1164 0.258667 1164 0.894330 CUST_GENDER 1041 0.014623 CUST_GENDER IN F\n81 2 1164 0.258667 1164 0.894330 CUST_GENDER 1041 0.014623 CUST_GENDER IN M\n82 2 1164 0.258667 1164 0.894330 CUST_YEAR_OF_BIRTH 1041 0.135051 CUST_YEAR_OF_BIRTH <= 1986\n83 2 1164 0.258667 1164 0.894330 CUST_YEAR_OF_BIRTH 1041 0.135051 CUST_YEAR_OF_BIRTH > 1971.4\n84 2 1164 0.258667 1164 0.894330 Y_BOX_GAMES 1041 0.480401 Y_BOX_GAMES <= 1\n85 2 1164 0.258667 1164 0.894330 Y_BOX_GAMES 1041 0.480401 Y_BOX_GAMES > 0\n86 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN < Bach.\n87 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN Assoc-A\n88 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN Assoc-V\n89 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN Bach.\n90 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN HS-grad\n91 3 3336 0.741333 2861 0.857614 EDUCATION 2861 0.000893 EDUCATION IN Masters\n92 3 3336 0.741333 2955 0.857614 YRS_RESIDENCE 2861 0.013965 YRS_RESIDENCE <= 7\n93 3 3336 0.741333 2955 0.857614 YRS_RESIDENCE 2861 0.013965 YRS_RESIDENCE > 2\n94 3 3336 0.741333 2958 0.857614 HOUSEHOLD_SIZE 2861 0.015623 HOUSEHOLD_SIZE IN 2\n95 3 3336 0.741333 2958 0.857614 HOUSEHOLD_SIZE 2861 0.015623 HOUSEHOLD_SIZE IN 3\n96 3 3336 0.741333 2958 0.857614 HOUSEHOLD_SIZE 2861 0.015623 HOUSEHOLD_SIZE IN 9+\n97 3 3336 0.741333 3025 0.857614 CUST_MARITAL_STATUS 2861 0.014089 CUST_MARITAL_STATUS IN Divorc.\n98 3 3336 0.741333 3025 0.857614 CUST_MARITAL_STATUS 2861 0.014089 CUST_MARITAL_STATUS IN Married\n99 3 3336 0.741333 3025 0.857614 CUST_MARITAL_STATUS 2861 0.014089 CUST_MARITAL_STATUS IN NeverM\n100 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n101 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n102 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n103 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n104 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n105 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n106 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n107 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n108 3 3336 0.741333 3028 0.857614 CUST_INCOME_LEVEL 2861 0.000263 CUST_INCOME_LEVEL IN L: 300,000 and above\n109 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN ?\n110 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Cleric.\n111 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Crafts\n112 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Exec.\n113 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Machine\n114 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Other\n115 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Prof.\n116 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Sales\n117 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN TechSup\n118 3 3336 0.741333 3048 0.857614 OCCUPATION 2861 0.001405 OCCUPATION IN Transp.\n119 3 3336 0.741333 3094 0.857614 Y_BOX_GAMES 2861 0.071001 Y_BOX_GAMES <= 0\n120 3 3336 0.741333 3094 0.857614 Y_BOX_GAMES 2861 0.071001 Y_BOX_GAMES >= 0\n121 3 3336 0.741333 3181 0.857614 CUST_YEAR_OF_BIRTH 2861 0.024159 CUST_YEAR_OF_BIRTH <= 1978.7\n122 3 3336 0.741333 3181 0.857614 CUST_YEAR_OF_BIRTH 2861 0.024159 CUST_YEAR_OF_BIRTH > 1934.9\n123 3 3336 0.741333 3336 0.857614 AFFINITY_CARD 2861 0.005412 AFFINITY_CARD <= 1\n124 3 3336 0.741333 3336 0.857614 AFFINITY_CARD 2861 0.005412 AFFINITY_CARD >= 0\n125 3 3336 0.741333 3336 0.857614 CUST_CREDIT_LIMIT 2861 0.000094 CUST_CREDIT_LIMIT <= 15000\n126 3 3336 0.741333 3336 0.857614 CUST_CREDIT_LIMIT 2861 0.000094 CUST_CREDIT_LIMIT >= 1500\n127 3 3336 0.741333 3336 0.857614 CUST_GENDER 2861 0.001996 CUST_GENDER IN F\n128 3 3336 0.741333 3336 0.857614 CUST_GENDER 2861 0.001996 CUST_GENDER IN M\n129 4 3155 0.701111 2379 0.754041 CUST_GENDER 2379 0.007041 CUST_GENDER = M\n130 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN < Bach.\n131 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN Assoc-A\n132 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN Assoc-V\n133 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN Bach.\n134 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN HS-grad\n135 4 3155 0.701111 2702 0.754041 EDUCATION 2379 0.000859 EDUCATION IN Masters\n136 4 3155 0.701111 2801 0.754041 YRS_RESIDENCE 2379 0.015000 YRS_RESIDENCE <= 7\n137 4 3155 0.701111 2801 0.754041 YRS_RESIDENCE 2379 0.015000 YRS_RESIDENCE > 2\n138 4 3155 0.701111 2846 0.754041 CUST_MARITAL_STATUS 2379 0.012734 CUST_MARITAL_STATUS IN Divorc.\n139 4 3155 0.701111 2846 0.754041 CUST_MARITAL_STATUS 2379 0.012734 CUST_MARITAL_STATUS IN Married\n140 4 3155 0.701111 2846 0.754041 CUST_MARITAL_STATUS 2379 0.012734 CUST_MARITAL_STATUS IN NeverM\n141 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN B: 30,000 - 49,999\n142 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n143 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n144 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n145 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n146 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n147 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n148 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n149 4 3155 0.701111 2857 0.754041 CUST_INCOME_LEVEL 2379 0.000231 CUST_INCOME_LEVEL IN L: 300,000 and above\n150 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN ?\n151 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Cleric.\n152 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Crafts\n153 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Exec.\n154 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Machine\n155 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Other\n156 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Prof.\n157 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Sales\n158 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN TechSup\n159 4 3155 0.701111 2873 0.754041 OCCUPATION 2379 0.001716 OCCUPATION IN Transp.\n160 4 3155 0.701111 2913 0.754041 Y_BOX_GAMES 2379 0.067727 Y_BOX_GAMES <= 0\n161 4 3155 0.701111 2913 0.754041 Y_BOX_GAMES 2379 0.067727 Y_BOX_GAMES >= 0\n162 4 3155 0.701111 2958 0.754041 HOUSEHOLD_SIZE 2379 0.020523 HOUSEHOLD_SIZE IN 2\n163 4 3155 0.701111 2958 0.754041 HOUSEHOLD_SIZE 2379 0.020523 HOUSEHOLD_SIZE IN 3\n164 4 3155 0.701111 2958 0.754041 HOUSEHOLD_SIZE 2379 0.020523 HOUSEHOLD_SIZE IN 9+\n165 4 3155 0.701111 3000 0.754041 CUST_YEAR_OF_BIRTH 2379 0.023531 CUST_YEAR_OF_BIRTH <= 1978.7\n166 4 3155 0.701111 3000 0.754041 CUST_YEAR_OF_BIRTH 2379 0.023531 CUST_YEAR_OF_BIRTH > 1934.9\n167 4 3155 0.701111 3155 0.754041 AFFINITY_CARD 2379 0.003698 AFFINITY_CARD <= 1\n168 4 3155 0.701111 3155 0.754041 AFFINITY_CARD 2379 0.003698 AFFINITY_CARD >= 0\n169 4 3155 0.701111 3155 0.754041 CUST_CREDIT_LIMIT 2379 0.000101 CUST_CREDIT_LIMIT <= 15000\n170 4 3155 0.701111 3155 0.754041 CUST_CREDIT_LIMIT 2379 0.000101 CUST_CREDIT_LIMIT >= 1500\n171 5 181 0.040222 157 0.867403 YRS_RESIDENCE 157 0.011292 YRS_RESIDENCE <= 5\n172 5 181 0.040222 157 0.867403 YRS_RESIDENCE 157 0.011292 YRS_RESIDENCE > 1\n173 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN < Bach.\n174 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN Assoc-A\n175 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN Assoc-V\n176 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN Bach.\n177 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN HS-grad\n178 5 181 0.040222 159 0.867403 EDUCATION 157 0.003645 EDUCATION IN Masters\n179 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN E: 90,000 - 109,999\n180 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN F: 110,000 - 129,999\n181 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN G: 130,000 - 149,999\n182 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN H: 150,000 - 169,999\n183 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN I: 170,000 - 189,999\n184 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN J: 190,000 - 249,999\n185 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN K: 250,000 - 299,999\n186 5 181 0.040222 166 0.867403 CUST_INCOME_LEVEL 157 0.005944 CUST_INCOME_LEVEL IN L: 300,000 and above\n187 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN ?\n188 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Cleric.\n189 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Exec.\n190 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Machine\n191 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Other\n192 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Prof.\n193 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN Sales\n194 5 181 0.040222 166 0.867403 OCCUPATION 157 0.019188 OCCUPATION IN TechSup\n195 5 181 0.040222 170 0.867403 CUST_YEAR_OF_BIRTH 157 0.054366 CUST_YEAR_OF_BIRTH <= 1971.4\n196 5 181 0.040222 170 0.867403 CUST_YEAR_OF_BIRTH 157 0.054366 CUST_YEAR_OF_BIRTH > 1942.2\n197 5 181 0.040222 179 0.867403 CUST_MARITAL_STATUS 157 0.125248 CUST_MARITAL_STATUS = Married\n198 5 181 0.040222 179 0.867403 HOUSEHOLD_SIZE 157 0.320732 HOUSEHOLD_SIZE = 4-5\n199 5 181 0.040222 181 0.867403 AFFINITY_CARD 157 0.078669 AFFINITY_CARD <= 1\n200 5 181 0.040222 181 0.867403 AFFINITY_CARD 157 0.078669 AFFINITY_CARD >= 0\n201 5 181 0.040222 181 0.867403 CUST_CREDIT_LIMIT 157 0.000944 CUST_CREDIT_LIMIT <= 15000\n202 5 181 0.040222 181 0.867403 CUST_CREDIT_LIMIT 157 0.000944 CUST_CREDIT_LIMIT >= 1500\n203 5 181 0.040222 181 0.867403 CUST_GENDER 157 0.456930 CUST_GENDER = F\n204 5 181 0.040222 181 0.867403 Y_BOX_GAMES 157 0.177207 Y_BOX_GAMES <= 0\n205 5 181 0.040222 181 0.867403 Y_BOX_GAMES 157 0.177207 Y_BOX_GAMES >= 0\n\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Display model settings","message":["%python","","em_mod.settings"],"enabled":true,"result":{"startTime":1713804695408,"interpreter":"python.low","endTime":1713804695470,"results":[{"message":"{'emcs_num_iterations': 20, 'emcs_random_seed': 7, 'emcs_cluster_components': 'EMCS_CLUSTER_COMP_ENABLE', 'emcs_cluster_statistics': 'EMCS_CLUS_STATS_ENABLE', 'emcs_cluster_thresh': 2, 'emcs_linkage_function': 'EMCS_LINKAGE_SINGLE', 'emcs_loglike_improvement': 0.001, 'emcs_max_num_attr_2d': 50, 'emcs_min_pct_attr_support': 0.1, 'emcs_model_search': 'EMCS_MODEL_SEARCH_DISABLE', 'emcs_num_components': 20, 'emcs_num_distribution': 'EMCS_NUM_DISTR_SYSTEM', 'emcs_num_equiwidth_bins': 11, 'emcs_num_projections': 50, 'emcs_remove_components': 'EMCS_REMOVE_COMPS_ENABLE', 'emcs_attribute_filter': 'EMCS_ATTR_FILTER_ENABLE', 'emcs_convergence_criterion': 'EMCS_CONV_CRIT_HELDASIDE', 'emcs_num_quantile_bins': 11, 'emcs_num_topn_bins': 15, 'odms_details': 'ODMS_ENABLE', 'odms_sampling': 'ODMS_SAMPLING_DISABLE', 'prep_auto': 'ON', 'clus_num_clusters': 3}\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display cluster details for all clusters in the hierarchy with row counts and dispersion","message":["%python","","z.show(em_mod.clusters)"],"enabled":true,"result":{"startTime":1713804695544,"interpreter":"python.low","endTime":1713804695640,"results":[{"message":"CLUSTER_ID\tCLUSTER_NAME\tRECORD_COUNT\tPARENT\tTREE_LEVEL\tLEFT_CHILD_ID\tRIGHT_CHILD_ID\n1.0\t1.0\t4500.0\tnan\t1.0\t2.0\t3.0\n2.0\t2.0\t1164.0\t1.0\t2.0\tnan\tnan\n3.0\t3.0\t3336.0\t1.0\t2.0\t4.0\t5.0\n4.0\t4.0\t3155.0\t3.0\t3.0\tnan\tnan\n5.0\t5.0\t181.0\t3.0\t3.0\tnan\tnan\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Apply model to assign rows to clusters","message":["%python","","pred = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF)"],"enabled":true,"result":{"startTime":1713804695714,"interpreter":"python.low","endTime":1713804695832,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Use matplotlib scatterplot to view some cluster results","message":["%python","","pred_df = pred[['CUST_ID', 'CLUSTER_ID', 'CUST_YEAR_OF_BIRTH', 'YRS_RESIDENCE', 'CUST_CREDIT_LIMIT']].pull()","","fig = plt.figure()","","plt.style.use('seaborn')","","ax=fig.add_axes([0.1,0.1,0.8,0.8])","","ids = pred_df['CLUSTER_ID']","clusters = ids.drop_duplicates().values","handles = []","labs = []","colors = ['r', 'b', 'g']","for i, c in enumerate(clusters):"," xc = pred_df[pred_df['CLUSTER_ID'] == c]['YRS_RESIDENCE'].values"," yc = pred_df[pred_df['CLUSTER_ID'] == c]['CUST_YEAR_OF_BIRTH'].values"," "," h = ax.scatter(xc, yc, color= colors[i])"," handles.append(h)"," labs.append('CLUSTER' + str(c))","ax.legend(handles, labs)","plt.title('EM Clustering')","","plt.grid(True)","","plt.xlabel('YRS_RESIDENCE')","plt.ylabel('CUST_YEAR_OF_BIRTH')","plt.show()"],"enabled":true,"result":{"startTime":1713804695904,"interpreter":"python.low","endTime":1713804696175,"results":[{"message":"<div style='width:auto;height:auto'><img 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 style='width=auto;height:auto'><div>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Compare clusters","","Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals. "],"enabled":true,"result":{"startTime":1713804696260,"interpreter":"md.low","endTime":1713804696344,"results":[{"message":"<h3 id=\"compare-clusters\">Compare clusters<\/h3>\n<p>Compare the feature distribution for year of birth in clusters 4 and 5. Cluster 4 tends to have middle aged individuals, while cluster 5 has more senior individuals.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 4","message":["%python","","feature = 'CUST_YEAR_OF_BIRTH' ","","DF = pred[pred['CLUSTER_ID'] == 4].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713804696419,"interpreter":"python.low","endTime":1713804696742,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1913\t5\n1921\t1\n1922\t6\n1923\t4\n1924\t6\n1925\t8\n1926\t12\n1927\t3\n1928\t14\n1929\t7\n1930\t4\n1931\t7\n1932\t13\n1933\t10\n1934\t18\n1935\t18\n1936\t22\n1937\t12\n1938\t26\n1939\t23\n1940\t28\n1941\t31\n1942\t28\n1943\t41\n1944\t51\n1945\t47\n1946\t45\n1947\t41\n1948\t50\n1949\t50\n1950\t56\n1951\t49\n1952\t76\n1953\t53\n1954\t91\n1955\t91\n1956\t91\n1957\t109\n1958\t100\n1959\t110\n1960\t95\n1961\t109\n1962\t107\n1963\t94\n1964\t97\n1965\t88\n1966\t112\n1967\t114\n1968\t125\n1969\t121\n1970\t103\n1971\t115\n1972\t87\n1973\t89\n1974\t51\n1975\t61\n1976\t28\n1977\t44\n1978\t21\n1979\t19\n1980\t6\n1981\t5\n1982\t2\n1983\t5\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"bar\":{\"showSeries\":[\"count\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"count\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"CUST_YEAR_OF_BIRTH\",\"count\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Distribution of the year of birth for cluster 5","message":["%python","","DF = pred[pred['CLUSTER_ID'] == 5].crosstab([feature])","z.show(DF.sort_values(feature))"],"enabled":true,"result":{"startTime":1713804696821,"interpreter":"python.low","endTime":1713804697143,"results":[{"message":"CUST_YEAR_OF_BIRTH\tcount\n1939\t2\n1940\t2\n1943\t2\n1944\t1\n1945\t2\n1947\t4\n1948\t2\n1949\t1\n1950\t4\n1951\t2\n1952\t7\n1953\t3\n1954\t2\n1955\t3\n1956\t5\n1957\t10\n1958\t5\n1959\t9\n1960\t5\n1961\t6\n1962\t6\n1963\t8\n1964\t8\n1965\t15\n1966\t6\n1967\t9\n1968\t12\n1969\t14\n1970\t13\n1971\t6\n1972\t4\n1973\t3\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Check cluster assignment probabilities for top cluster assignments","message":["%python","","pred_details = em_mod.predict_proba(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN=3 )","","z.show(pred_details.round(8).head())"],"enabled":true,"result":{"startTime":1713804697221,"interpreter":"python.low","endTime":1713804697946,"results":[{"message":"CUST_ID\tTOP_1\tTOP_1_PROB\tTOP_2\tTOP_2_PROB\tTOP_3\tTOP_3_PROB\n100134.0\t4.0\t0.9999999\t5.0\t1e-07\t2.0\t0.0\n102828.0\t4.0\t0.99999317\t5.0\t6.83e-06\t2.0\t0.0\n101232.0\t2.0\t0.99999999\t4.0\t1e-08\t5.0\t0.0\n100696.0\t4.0\t0.99999998\t5.0\t2e-08\t2.0\t0.0\n103948.0\t4.0\t1.0\t5.0\t0.0\t2.0\t0.0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Accessing the prediction details for each cluster prediction","message":["%python","","pred_test = em_mod.predict(CUST_DF, supplemental_cols = CUST_DF['CUST_ID'], topN_attrs=3 )","","z.show(pred_test.round(8).head())"],"enabled":true,"result":{"startTime":1713804698028,"interpreter":"python.low","endTime":1713804703044,"results":[{"message":"CUST_ID\tCLUSTER_ID\tNAME_1\tVALUE_1\tWEIGHT_1\tNAME_2\tVALUE_2\tWEIGHT_2\tNAME_3\tVALUE_3\tWEIGHT_3\n100100\t5\tHOUSEHOLD_SIZE\t4-5\t.001\tCUST_GENDER\tF\t.001\tNone\tNone\tNone\n100200\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n100500\t2\tCUST_YEAR_OF_BIRTH\t1983\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101200\t2\tCUST_YEAR_OF_BIRTH\t1984\t1\tNone\tNone\tNone\tNone\tNone\tNone\n101500\t2\tCUST_YEAR_OF_BIRTH\t1981\t1\tNone\tNone\tNone\tNone\tNone\tNone\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## Use SQL to access prediction details","One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.","","We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view. ","","Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well. "],"enabled":true,"result":{"startTime":1713804703122,"interpreter":"md.low","endTime":1713804703184,"results":[{"message":"<h2 id=\"use-sql-to-access-prediction-details\">Use SQL to access prediction details<\/h2>\n<p>One feature of OML is the ability to use models from SQL as well as Python. Prediction details are exposed through the Python API and are available through the SQL API, which is illustrated here.<\/p>\n<p>We first create a table from the Python proxy object CUSTOMERS360 so that SQL can reference it. This could just as easily been a database view.<\/p>\n<p>Then, since prediction details are provided as an XML string, we extract the elements to present them as table columns. The 'first attribute' identfies the feature and value that most influences the cluster assignment with corresponding weight. This is followed by the second and third attribute. In this example, we provide three, but additional attributes could be presented as well.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create table for use in SQL query","message":["%python","","try:"," oml.drop(table = 'EM_CUSTOMERS360')","except:"," pass","_ = CUST_DF.materialize(table = 'EM_CUSTOMERS360')"],"enabled":true,"result":{"startTime":1713804703261,"interpreter":"python.low","endTime":1713804704457,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List 3 most relevant attributes for specific customers and likely cluster assignments","message":["%sql","","SELECT CUST_ID,"," CLUSTER_ID,"," ROUND(PROB*100,0) PROB_PCT,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID, S.CLUSTER_ID, PROBABILITY PROB, "," CLUSTER_DETAILS(CUST_CLUSTER_MODEL USING T.*) 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