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

IndexAactivation function, 234acuity, 258AdaBoost, 328AdaBoost.M1, 321, 416Add, 395AddCluster, 396, 397AddExpression, 397additive logistic regression, 327–328additive regression, 325–327AdditiveRegression, 416AddNoise, 400AD (all-dimensions) tree, 280–283ADTree, 408advanced methods. See implementation—real-world schemesadversarial data mining, 356–358aggregation, appropriate degree in datawarehousing, 53Akaike Information Criterion (AIC), 277Alberta Ingenuity Centre for MachineLearning, 38algorithmsadditive logistic regression, 327advanced methods, 187–283. See alsoimplementation—real-world schemesassociation rule mining, 112–119bagging, 319basic methods, 83–142. See also algorithmsbasicmethodsBayesian network learning, 277–283clustering, 136–139clustering in Weka, 418–419covering, 105–112decision tree induction, 97–105divide-and-conquer, 107EM, 265–266expanding examples into partial tree, 208filtering in Weka, 393–403. See also filteringalgorithmsincremental, 346instance-based learning, 128–136learning in Weka, 403–414. See also learningalgorithmslinear models, 119–128metalearning in Weka, 414–4181R method, 84–88perceptron learning rule, 124RIPPER rule learner, 206rule formation-incremental reduced-errorpruning, 205separate-and-conquer, 112statistical modeling, 88–97stochastic, 348Winnow, 127See also individual subject headings.all-dimensions (AD) tree, 280–283alternating decision tree, 329, 330, 343Analyze panel, 443–445analyzing purchasing patterns, 27ancestor-of, 48anomalies, 314–315505