Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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5 1 0 I N D E X CSVLoader, 381 cumulative margin distribution in Weka, 458 curves cost, 173 lift, 166 recall-precision, 171 ROC, 168 customer support and service, 28 cutoff parameter, 260
cybersecurity, 29 D dairy farmers (New Zealand), 3–4, 37, 161–162 data assembly, 52–53 data cleaning, 52–60. See also automatic data cleansing data engineering. See engineering input and output data integration, 52 data mining, 4–5, 9 data ownership rights, 35 data preparation, 52–60 data transformation. See attribute transformations
data warehouse, 52–53 date attributes, 55 decision list, 11, 67 decision nodes, 328 decision stump, 325 DecisionStump, 407, 453, 454 decision table, 62, 295 DecisionTable, 408 decision tree, 14, 62–65, 97–105 complexity of induction, 196 converting to rules, 198 data cleaning, 312–313 error rates, 192–196 highly branching attributes, 102–105 missing values, 63, 191–192 multiclass case, 107 multivariate, 199 nominal attribute, 62 numeric attribute, 62, 189–191 partial, 207–210 pruning, 192–193, 312 replicated subtree, 66 rules, 198 subtree raising, 193, 197 subtree replacement, 192–193, 197 three-way split, 63 top-down induction, 97–105, 196–198 two-way split, 62 univariate, 199 Weka, 406–408 Weka’s User Classifer facility, 63–65 Decorate, 416 deduction, 350 default rule, 110 degrees of freedom, 93, 155 delta, 311 dendrograms, 82 denormalization, 47 density function, 93 diagnosis, 25–26 dichotomy, 51 directed acyclic graph, 272 direct marketing, 27 discrete attributes, 50. See also nominal attributes Discretize, 396, 398, 402 discretizing numeric attributes, 287, 296–305 chi-squared test, 302 converting discrete to numeric attributes, 304–305 entropy-based discretization, 298–302 error-based discretization, 302–304 global discretization, 297 local discretization, 297 supervised discretization, 297, 298 unsupervised discretization, 297–298 Weka, 398 disjunction, 32, 65 disjunctive normal form, 69 distance functions, 128–129, 239–242 distributed experiments in Weka, 445 distribution, 304
divide-and-conquer. See decision tree P088407-INDEX.qxd 4/30/05 11:25 AM Page 510
I N D E X 5 1 1
document classification, 94–96, 352–353 document clustering, 353 domain knowledge, 20, 33, 349–351 double-consequent rules, 118 duplicate data, 59 dynamic programming, 302 E early stopping, 233 easy instances, 322 ecological applications, 23, 28 eigenvalue, 307 eigenvector, 307 Einstein, Albert, 180 electricity supply, 24–25 electromechanical diagnosis application, 144
11-point average recall, 172 EM, 418 EM algorithm, 265–266 EM and co-training, 340–341 EM procedure, 337–338 embedded machine learning, 461–469 engineering input and output, 285–343 attribute selection, 288–296 combining multiple models, 315–336 data cleansing, 312–315 discretizing numeric attributes, 296–305 unlabeled data, 337–341
entity extraction, 353 entropy, 102 entropy-based discretization, 298–302 enumerated attributes, 50. See also nominal attributes enumerating the concept space, 31–32 Epicurus, 183 epoch, 412 equal-frequency binning, 298 equal-interval binning, 298 equal-width binning, 342 erroneous values, 59 error-based discretization, 302–304 error-correcting output codes, 334–336 error log, 378 error rate bias, 317 cost of errors. See cost of errors decision tree, 192–196 defined, 144 training data, 145 “Essay towards solving a problem in the doctrine of chances, An” (Bayes), 141 ethics, 35–37 Euclidean distance, 78, 128, 129, 237 evaluation, 143–185 bootstrap procedure, 152–153 comparing data mining methods, 153–157 cost of errors, 161–176. See also cost of errors cross-validation, 149–152 leave-one-out cross-validation, 151–152 MDL principle, 179–184 numeric prediction, 176–179 predicting performance, 146–149 predicting probabilities, 157–161 training and testing, 144–146 evaluation(), 482 evaluation components in Weka, 430, 431 Evaluation panel, 431 example problems contact lens data, 6, 13–15 CPU performance data, 16–17 iris dataset, 15–16 labor negotiations data, 17–18, 19 soybean data, 18–22 weather problem, 10–12 exceptions, 70–73, 210–213 exclusive-or problem, 67 exemplar defined, 236 generalized, 238–239 noisy, 236–237 redundant, 236 exemplar generalization, 238–239, 243 ExhaustiveSearch, 424 Expand all paths, 408 expectation, 265, 267 expected error, 174 expected success rate, 147 P088407-INDEX.qxd 4/30/05 11:25 AM Page 511
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