Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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P088407-REF.qxd 4/30/05 11:24 AM Page 503 P088407-REF.qxd 4/30/05 11:24 AM Page 504 A activation function, 234 acuity, 258 AdaBoost, 328 AdaBoost.M1, 321, 416 Add, 395 AddCluster, 396, 397 AddExpression, 397 additive logistic regression, 327–328 additive regression, 325–327
AD (all-dimensions) tree, 280–283 ADTree, 408 advanced methods. See implementation— real-world schemes adversarial data mining, 356–358 aggregation, appropriate degree in data warehousing, 53 Akaike Information Criterion (AIC), 277 Alberta Ingenuity Centre for Machine Learning, 38 algorithms additive logistic regression, 327 advanced methods, 187–283. See also implementation—real-world schemes association rule mining, 112–119 bagging, 319 basic methods, 83–142. See also algorithms- basic methods Bayesian network learning, 277–283 clustering, 136–139 clustering in Weka, 418–419 covering, 105–112 decision tree induction, 97–105 divide-and-conquer, 107 EM, 265–266 expanding examples into partial tree, 208 filtering in Weka, 393–403. See also filtering algorithms incremental, 346 instance-based learning, 128–136 learning in Weka, 403–414. See also learning algorithms linear models, 119–128 metalearning in Weka, 414–418 1R method, 84–88 perceptron learning rule, 124 RIPPER rule learner, 206 rule formation-incremental reduced-error pruning, 205 separate-and-conquer, 112 statistical modeling, 88–97 stochastic, 348 Winnow, 127 See also individual subject headings. all-dimensions (AD) tree, 280–283 alternating decision tree, 329, 330, 343
analyzing purchasing patterns, 27 ancestor-of, 48 anomalies, 314–315 Index 5 0 5
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