Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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5 1 4 I N D E X implementation—real-world schemes (continued) numeric prediction, 243–254
inaccurate values, 59–60. See also cost of errors; data cleaning; error rate incremental algorithms, 346 incrementalClassifier, 434 IncrementalClassifierEvaluator, 431 incremental clustering, 255–260 incremental learning in Weka, 433–435 incremental reduced-error pruning, 203, 205 independent attributes, 267
induction, 29 inductive logic programming, 48, 60, 75, 351 Induct system, 214 industrial usage. See implementation—real- world schemes inferring rudimentary rules, 84–88
informational loss function, 159–160, 161 information-based heuristic, 201 information extraction, 354 information gain, 99 information retrieval, 171 information value, 102 infrequent words, 353 inner cross-validation, 286 input, 41–60 ARFF format, 53–55 assembling the data, 52–53 attribute, 49–52 attribute types, 56–57 concept, 42–45 data engineering, 286–287, 288–315. See also engineering input and output data preparation, 52–60 getting to know your data, 60 inaccurate values, 59–60 instances, 45 missing values, 58 sparse data, 55–56 input layer, 224 instance in Weka, 450
instance-based learning, 78, 128–136, 235–243 ball tree, 133–135 distance functions, 128–129, 239–242 finding nearest neighbors, 129–135 generalized distance functions, 241–242 generalized exemplars, 236
missing values, 129 pruning noisy exemplars, 236–237 redundant exemplars, 236 simple method, 128–136, 235–236 weighting attributes, 237–238 Weka, 413–414 instance-based learning methods, 291 instance-based methods, 34 instance-based representation, 76–80 instance filters in Weka, 394, 400–401, 403 instances, 45 Instances, 451 instance space, 79 instance weights, 166, 321–322 integer-valued attributes, 49 intensive care patients, 29 interval, 88 interval quantities, 50–51 intrusion detection systems, 357 invertSelection, 382 in vitro fertilization, 3 iris dataset, 15–16 iris setosa, 15 iris versicolor, 15 iris virginica, 15 ISO-8601 combined date and time format, 55 item, 113 item sets, 113, 114–115 iterative distance-based clustering, 137–138
J4.8, 373–377 J48, 404, 450 Javadoc indices, 456 JDBC database, 445 JRip, 409 junk email filtering, 356–357 P088407-INDEX.qxd 4/30/05 11:25 AM Page 514
I N D E X 5 1 5
K K2, 278
Kappa statistic, 163–164 kD-trees, 130–132, 136 Kepler’s three laws of planetary motion, 180 kernel defined, 235 perceptron, 223 polynomial, 218 RBF, 219 sigmoid, 219 kernel density estimation, 97 kernel logistic regression, 223 kernel perceptron, 222–223
Knowledge Flow interface, 427–435 configuring/connecting components, 431–433
evaluation components, 430, 431 incremental learning, 433–435 starting up, 427 visualization components, 430–431 knowledge representation, 61–82 association rules, 69–70. See also association rules classification rules, 65–69. See also classification rules clusters, 81–82. See also clustering decision table, 62 decision tree, 62–65. See also decision tree instance-based representation, 76–80 rules involving relations, 73–75 rules with exceptions, 70–73, 210–213 trees for numeric prediction, 76 KStar, 413 L labor negotiations data, 17–18, 19 language bias, 32–33 language identification, 353 Laplace, Pierre, 91 Laplace estimator, 91, 267, 269 large datasets, 346–349 law of diminishing returns, 347 lazy classifiers in Weka, 405, 413–414
learning, 7–9 learning algorithms in Weka, 403–404 algorithm, listed, 404–405 Bayesian classifier, 403–406 functions, 404–405, 409–410 lazy classifiers, 405, 413–414 miscellaneous classifiers, 405, 414 neural network, 411–413 rules, 404, 408–409 trees, 404, 406–408 learning rate, 229, 230 least-absolute-error regression, 220
leave-one-out cross-validation, 151–152 levels of measurement, 50 level-0 model, 332 level-1 model, 332
lift chart, 166–168, 172 lift factor, 166 linear classification, 121–128 linearly separable, 124 linear machine, 142 linear models, 119–128, 214–235 backpropagation, 227–233 computational complexity, 218 kernel perceptron, 222–223 linear classification, 121–128 linear regression, 119–121 logistic regression, 121–125 maximum margin hyperplane, 215–217 multilayer perceptrons, 223–226, 231, 233 nonlinear class boundaries, 217–219 numeric prediction, 119–120 overfitting, 217–218 perceptron, 124–126 RBF network, 234 support vector regression, 219–222 Winnow, 126–128 linear regression, 77, 119–121
linear threshold unit, 142 P088407-INDEX.qxd 4/30/05 11:25 AM Page 515
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