Data Mining. Concepts and Techniques, 3rd Edition
HAN 22-ind-673-708-9780123814791
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HAN 22-ind-673-708-9780123814791 2011/6/1 3:27 Page 690 #18 690 Index icon-based visualization (Continued) stick figure technique, 61–63
ID3, 332, 385 greedy approach, 332 information gain, 336 See also decision tree induction IF-THEN rules, 355–357 accuracy, 356 conflict resolution strategy, 356 coverage, 356 default rule, 357 extracting from decision tree, 357 form, 355 rule antecedent, 355 rule consequent, 355 rule ordering, 357 satisfied, 356 triggered, 356 illustrated, 149 image data analysis, 319 imbalance problem, 367 imbalance ratio (IR), 270 skewness, 271 inconvertible constraints, 300 incremental data mining, 31 indexes bitmapped join, 163 composite join, 162 Gini, 332, 341–343 inverted, 212, 213 indexing
bitmap, 160–161, 179 bitmapped join, 179 frequent pattern mining for, 319 join, 161–163, 179 OLAP, 160–163 inductive databases, 601 inferential statistics, 24 information age, moving toward, 1–2 information extraction systems, 430 information gain, 336–340 decision tree induction using, 338–339 ID3 use of, 336 pattern frequency support versus, 421 single feature plot, 420 split-point, 340 information networks analysis, 592–593 evolution of, 594 link prediction in, 593–594 mining, 623 OLAP in, 594 role discovery in, 593–594 similarity search in, 594 information processing, 153 information retrieval (IR), 26–27 challenges, 27 language model, 26 topic model, 26–27 informativeness model, 535 initial working relations, 168, 169, 177 instance-based learners. See lazy learners instances, constraints on, 533, 539 integrated data warehouses, 126 integrators, 127 intelligent query answering, 618 interactive data mining, 604, 607 interactive mining, 30 intercuboid query expansion, 221 example, 224–225 method, 223–224 interdimensional association rules, 288 interestingness, 21–23 assessment methods, 23 components of, 21 expected, 22 objective measures, 21–22 strong association rules, 264–265 subjective measures, 22 threshold, 21–22 unexpected, 22 interestingness constraints, 294 application of, 297 interpretability backpropagation and, 406–408 classification, 369 cluster analysis, 447 data, 85 data quality and, 85 probabilistic hierarchical clustering, 469
interquartile range (IQR), 49, 555 interval-scaled attributes, 43, 79 intracuboid query expansion, 221 example, 223 method, 221–223 value usage, 222 intradimensional association rules, 287 intrusion detection, 569–570 anomaly-based, 614 data mining algorithms, 614–615 discriminative classifiers, 615 distributed data mining, 615 HAN 22-ind-673-708-9780123814791 2011/6/1 3:27 Page 691 #19 Index 691 signature-based, 614 stream data analysis, 615 visualization and query tools, 615 inverted indexes, 212, 213 invisible data mining, 33, 618–620, 625 IQR. See Interquartile range IR. See information retrieval item merging, 263 item skipping, 263 items, 13 itemsets, 246 candidate, 251, 252 dependent, 266 dynamic counting, 256 imbalance ratio (IR), 270, 271 negatively correlated, 292 occurrence independence, 266 strongly negatively correlated, 292
iterative Pattern-Fusion, 306 iterative relocation techniques, 448
Jaccard coefficient, 71 join indexing, 161–163, 179
Karush-Kuhn-Tucker (KKT) conditions, 412 k-distance neighborhoods, 565 kernel density estimation, 477–478 kernel function, 415
algorithm, 452 application of, 454 CLARANS, 457 within-cluster variation, 451, 452 clustering by, 453 drawback of, 454–455 functioning of, 452 scalability, 454 time complexity, 453 variants, 453–454
absolute-error criterion, 455 cost function for, 456 PAM, 455–457 k-nearest-neighbor classification, 423 closeness, 423 distance-based comparisons, 425 editing method, 425 missing values and, 424 number of neighbors, 424–425 partial distance method, 425 speed, 425 knowledge background, 30–31 mining, 29 presentation, 8 representation, 33 transfer, 434 knowledge bases, 5, 8 knowledge discovery data mining in, 7 process, 8 knowledge discovery from data (KDD), 6 knowledge extraction. See data mining knowledge mining. See data mining knowledge type constraints, 294 k-predicate sets, 289 Kulczynski measure, 268, 272 negatively correlated pattern based on, 293–294
language model, 26 Laplacian correction, 355 lattice of cuboids, 139, 156, 179, 188–189, 234 lazy learners, 393, 422–426, 437 case-based reasoning classifiers, 425–426 k-nearest-neighbor classifiers, 423–425 l-diversity method, 622 learning
active, 430, 433–434, 437 backpropagation, 400 as classification step, 328 connectionist, 398 by examples, 445 by observation, 445 rate, 397 semi-supervised, 572 supervised, 330 transfer, 430, 434–436, 438 unsupervised, 330, 445, 490 learning rates, 403–404 leave-one-out, 371 lift, 266, 272 correlation analysis with, 266–267 likelihood ratio statistic, 363 linear regression, 90, 105 multiple, 106 linearly, 412–413 linearly inseparable data, 413–415 Yüklə 7,95 Mb. Dostları ilə paylaş:
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