Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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5 0 6 I N D E X anomaly detection systems, 357 antecedent, of rule, 65 AODE, 405 Apriori, 419 Apriori method, 141 area under the curve (AUC), 173 ARFF format, 53–55 converting files to, 380–382 Weka, 370, 371 ARFFLoader, 381, 427 arithmetic underflow, 276 assembling the data, 52–53 assessing performance of learning scheme, 286 assignment of key phrases, 353
association learning, 43 association rules, 69–70, 112–119 binary attributes, 119 generating rules efficiently, 117–118 item sets, 113, 114–115 Weka, 419–420 association-rule learners in Weka, 419–420 attackers, 357
attribute discretization. See discretizing numeric attributes attribute-efficient, 128 attribute evaluation methods in Weka, 421, 422–423
attribute filters in Weka, 394, 395–400, 402–403 attributeIndices, 382 attribute noise, 313 attribute-relation file format. See ARFF format attributes, 49–52 adding irrelevant, 288 Boolean, 51 class, 53 as columns in tables, 49 combinations of, 65 continuous, 49 discrete, 51 enumerated, 51 highly branching, 86 identification code, 86 independent, 267 integer-valued, 49 nominal, 49 non-numeric, 17 numeric, 49 ordinal, 51 relevant, 289 rogue, 59 selecting, 288 subsets of values in, 80 types in ARFF format, 56 weighting, 237 See also orderings AttributeSelectedClassifier, 417 attribute selection, 286–287, 288–296 attribute evaluation methods in Weka, 421, 422–423
backward elimination, 292, 294 beam search, 293 best-first search, 293 forward selection, 292, 294 race search, 295 schemata search, 295 scheme-independent selection, 290–292 scheme-specific selection, 294–296 searching the attribute space, 292–294 search methods in Weka, 421, 423–425 Weka, 392–393, 420–425
attribute subset evaluators in Weka, 421, 422 AttributeSummarizer, 431 attribute transformations, 287, 305–311 principal components analysis, 306–309 random projections, 309 text to attribute vectors, 309–311 time series, 311 attribute weighting method, 237–238 AUC (area under the curve), 173 audit logs, 357 authorship ascription, 353 AutoClass, 269–270, 271 automatic data cleansing, 287, 312–315 anomalies, 314–315 improving decision trees, 312–313 robust regression, 313–314 P088407-INDEX.qxd 4/30/05 11:25 AM Page 506 I N D E X 5 0 7
automatic filtering, 315 averaging over subnetworks, 283 axis-parallel class boundaries, 242
background knowledge, 348 backpropagation, 227–233 backtracking, 209 backward elimination, 292, 294 backward pruning, 34, 192 bagging, 316–319
bagging with costs, 319–320 bag of words, 95 balanced Winnow, 128 ball tree, 133–135 basic methods. See algorithms-basic methods batch learning, 232 Bayes, Thomas, 141 Bayesian classifier. See Naïve Bayes Bayesian clustering, 268–270 Bayesian multinet, 279–280 Bayesian network, 141, 271–283 AD tree, 280–283 Bayesian multinet, 279–280 caveats, 276, 277 counting, 280 K2, 278 learning, 276–283 making predictions, 272–276 Markov blanket, 278–279 multiplication, 275 Naïve Bayes classifier, 278 network scoring, 277 simplifying assumptions, 272 structure learning by conditional independence tests, 280 TAN (Tree Augmented Naïve Bayes), 279 Weka, 403–406 Bayesian network learning algorithms, 277–283 Bayesian option trees, 328–331, 343 Bayesians, 141 Bayesian scoring metrics, 277–280, 283 Bayes information, 271
Bayes’s rule, 90, 181 beam search, 34, 293 beam width, 34 beer purchases, 27 Ben Ish Chai, 358 Bernoulli process, 147
best-first search, 293 best-matching node, 257 bias, 32
defined, 318 language, 32–33 multilayer perceptrons, 225, 226 overfitting-avoidance, 34–35 perceptron learning rule, 124 search, 33–34 what is it, 317 bias-variance decomposition, 317, 318 big data (massive datasets), 346–349 binning
equal-frequency, 298 equal-interval, 298 equal-width, 342 binomial coefficient, 218 bits, 102 boolean, 51, 68 boosting, 321–325, 347 boosting in Weka, 416 bootstrap aggregating, 318 bootstrap estimation, 152–153 British Petroleum, 28
C4.5, 105, 198–199 C5.0, 199 calm computing, 359, 362 capitalization conventions, 310 CAPPS (Computer Assisted Passenger Pre- Screening System), 357 CART (Classification And Regression Tree), 29, 38, 199, 253 categorical attributes, 49. See also nominal attributes category utility, 260–262 P088407-INDEX.qxd 4/30/05 11:25 AM Page 507
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