Data Mining: Practical Machine Learning Tools and ... - LIDeCC

INDEX 509randomization, 320–321stacking, 332–334command-line interface, 449–459class, 450, 452classifiers package, 453–455core package, 451, 452generic options, 456–458instance, 450Javadoc indices, 456package, 451, 452scheme-specific options, 458–459starting up, 449–450weka.associations, 455weka.attributeSelection, 455weka.clusterers, 455weka.estimators, 455weka.filters, 455command-line options, 456–459comma-separated value (CSV) format, 370,371comparing data mining methods, 153–157ComplementNaiveBayes, 405compression techniques, 362computational learning theory, 324computeEntropy(), 480Computer Assisted Passenger Pre-ScreeningSystem (CAPPS), 357computer network security, 357computer software. See Weka workbenchconcept, 42concept description, 42concept description language, 32concept representation, 82. See also knowledgerepresentationconditional independence, 275conditional likelihood for scoring networks,280, 283confidence, 69, 113, 324Confidence, 420confidence tests, 154–157, 184conflict resolution strategies, 82confusion matrix, 163conjunction, 65ConjunctiveRule, 408–409consensus filter, 342consequent, of rule, 65ConsistencySubsetEval, 422constrained quadratic optimization, 217consumer music, 359contact lens data, 6, 13–15continuous attributes, 49. See also numericattributescontinuous monitoring, 28–29converting discrete to numeric attributes,304–305convex hull, 171, 216Conviction, 420Copy, 395core package, 451, 452corrected resampled t-test, 157correlation coefficient, 177–179cost curves, 173–176cost matrix, 164–165cost of errors, 161–176bagging, 319–320cost curves, 173–176cost-sensitive classification, 164–165cost-sensitive learning, 165–166Kappa statistic, 163–164lift charts, 166–168recall-precision curves, 171–172ROC curves, 168–171cost-sensitive classification, 164–165CostSensitiveClassifier, 417cost-sensitive learning, 165–166cost-sensitive learning in Weka, 417co-training, 339–340covariance matrix, 267, 307coverage, of association rules, 69covering algorithm, 106–111cow culling, 3–4, 37, 161–162CPU performance data, 16–17credit approval, 22–23cross-validated ROC curves, 170cross-validation, 149–152, 326inner, 286outer, 286repeated, 144CrossValidationFoldMaker, 428, 431CSV format, 370, 371