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 698 #26 698 Index queries (Continued) subcube, 216, 217–218 top-k, 225–227 query languages, 31 query models, 149–150 query-driven approach, 128 querying function, 433 R rag bag criterion, 488 RainForest, 385 random forests, 382–383 random sampling, 370, 386 random subsampling, 370 random walk, 526 similarity based on, 527 randomization methods, 621 range, 48 interquartile, 49 range queries, 220 ranking cubes, 225–227, 235 dimensions, 225 function, 225 heterogeneous networks, 593 rare patterns, 280, 283, 320 example, 291–292 mining, 291–294 ratio-scaled attributes, 43–44, 79 reachability density, 566 reachability distance, 565 recall measure, 368–369 recognition rate, 366–367 recommender systems, 282, 615 advantages, 616 biclustering for, 514–515 challenges, 617 collaborative, 610, 615, 616, 617, 618 content-based approach, 615, 616 data mining and, 615–618 error types, 617–618 frequent pattern mining for, 319 hybrid approaches, 618 intelligent query answering, 618 memory-based methods, 617 use scenarios, 616 recursive partitioning, 335 reduced support, 285, 286 redundancy in data integration, 94 detection by correlations analysis, 94–98 redundancy-aware top-k patterns, 281, 311, 320 extracting, 310–312 finding, 312 strategy comparison, 311–312 trade-offs, 312 refresh, in back-end tools/utilities, 134 regression, 19, 90 coefficients, 105–106 example, 19 linear, 90, 105–106 in statistical data mining, 599 regression analysis, 19, 328 in time-series data, 587–588 relational databases, 9 components of, 9 mining, 10 relational schema for, 10 relational OLAP (ROLAP), 132, 164, 165, 179 relative significance, 312 relevance analysis, 19 repetition, 346 replication, 347 illustrated, 346 representative patterns, 309 retail industry, 609–611 RIPPER, 359, 363 robustness, classification, 369 ROC curves, 374, 386 classification models, 377 classifier comparison with, 373–377 illustrated, 376, 377 plotting, 375 roll-up operation, 11, 146 rough set approach, 428–429, 437 row enumeration, 302 rule ordering, 357 rule pruning, 363 rule quality measures, 361–363 rule-based classification, 355–363, 386 IF-THEN rules, 355–357 rule extraction, 357–359 rule induction, 359–363 rule pruning, 363 rule quality measures, 361–363 rules for constraints, 294 S sales campaign analysis, 610 samples, 218 cluster, 108–109 data, 219
HAN 22-ind-673-708-9780123814791 2011/6/1 3:27 Page 699 #27 Index 699 simple random, 108 stratified, 109–110 sampling
in Apriori efficiency, 256 as data redundancy technique, 108–110 methods, 108–110 oversampling, 384–385 random, 386 with replacement, 380–381 uncertainty, 433 undersampling, 384–385 sampling cubes, 218–220, 235 confidence interval, 219–220 framework, 219–220 query expansion with, 221 SAS Enterprise Miner, 603, 604 scalability classification, 369 cluster analysis, 446 cluster methods, 445 data mining algorithms, 31 decision tree induction and, 347–348 dimensionality and, 577 k-means, 454 scalable computation, 319 SCAN. See Structural Clustering Algorithm for Networks
core vertex, 531 illustrated, 532 scatter plots, 54 2-D data set visualization with, 59 3-D data set visualization with, 60 correlations between attributes, 54–56 illustrated, 55 matrix, 56, 59 schemas integration, 94 snowflake, 140–141 star, 139–140 science applications, 611–613 search engines, 28 search space pruning, 263, 301 second guess heuristic, 369 selection dimensions, 225 self-training, 432 semantic annotations applications, 317, 313, 320–321 with context modeling, 316 from DBLP data set, 316–317 effectiveness, 317 example, 314–315 of frequent patterns, 313–317 mutual information, 315–316 task definition, 315 Semantic Web, 597 semi-offline materialization, 226 semi-supervised classification, 432–433, 437 alternative approaches, 433 cotraining, 432–433 self-training, 432 semi-supervised learning, 25 outlier detection by, 572 semi-supervised outlier detection, 551 sensitivity analysis, 408 sensitivity measure, 367 sentiment classification, 434 sequence data analysis, 319 sequences, 586 alignment, 590 biological, 586, 590–591 classification of, 589–590 similarity searches, 587 symbolic, 586, 588–590 time-series, 586, 587–588 sequential covering algorithm, 359 general-to-specific search, 360 greedy search, 361 illustrated, 359 rule induction with, 359–361 sequential pattern mining, 589 constraint-based, 589 in symbolic sequences, 588–589 shapelets method, 590 shared dimensions, 204 pruning, 205 shared-sorts, 193 shared-partitions, 193 shell cubes, 160 shell fragments, 192, 235 approach, 211–212 computation algorithm, 212, 213 computation example, 214–215 precomputing, 210 shrinking diameter, 592 sigmoid function, 402 signature-based detection, 614 significance levels, 373 significance measure, 312 significance tests, 372–373, 386 silhouette coefficient, 489–490 similarity asymmetric binary, 71 cosine, 77–78
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