Data mining: concepts and techniques
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
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Amsterdam [u.a.]
Morgan Kaufmann Publishers
2023
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| Ausgabe: | fourth edition |
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxix, 752 Seiten Illustrationen, Diagramme |
| ISBN: | 9780128117606 |
Internformat
MARC
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| 100 | 1 | |a Han, Jiawei |d 1949- |e Verfasser |0 (DE-588)137798342 |4 aut | |
| 245 | 1 | 0 | |a Data mining |b concepts and techniques |c Jiawei Han ; Hanghang tong ; Jian Pei |
| 250 | |a fourth edition | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Morgan Kaufmann Publishers |c 2023 | |
| 300 | |a xxix, 752 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 700 | 1 | |a Pei, Jian |e Verfasser |0 (DE-588)1021437980 |4 aut | |
| 700 | 1 | |a Tong, Hanghang |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)135615844 |4 aut | |
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Datensatz im Suchindex
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| adam_text | Contents Foreword ..................................................................................................................................... Foreword to second edition......................................................................................................... Preface......................................................................................................................................... Acknowledgments ...................................................................................................................... About the authors........................................................................................................................ CHAPTER 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 CHAPTER 2 2.1 2.2 xvii xix xxi xxvii xxix Introduction.............................................................................................. What is data mining?........................................................................................ Data mining: an essential step in knowledge discovery................................... Diversity of data types for data mining............................................................. Mining various kinds of knowledge................................................................. 1.4.1 Multidimensional data summarization................................................. 1.4.2 Mining frequent patterns, associations, and correlations...................... 1.4.3 Classification and regression for predictive analysis............................ 1.4.4 Cluster
analysis..................................................................................... 1.4.5 Deep learning ....................................................................................... 1.4.6 Outlier analysis..................................................................................... 1.4.7 Are all mining results interesting? ........................................................ Data mining: confluence of multiple disciplines.............................................. 1.5.1 Statistics and data mining .................................................................... 1.5.2 Machine learning and data mining........................................................ 1.5.3 Database technology and data mining ................................................. 1.5.4 Data mining and data science................................................................ 1.5.5 Data mining and other disciplines ........................................................ Data mining and applications........................................................................... Data mining and society.................................................................................... Summary........................................................................................................... Exercises........................................................................................................... Bibliographic notes.......................................................................................... 1 1 2 4 5 6 6 7 9 9 10 10 12 12 13 15 15 16 17 19 19 20 21
Data, measurements, and data preprocessing............................................ Datatypes......................................................................................................... 2.1.1 Nominal attributes................................................................................. 2.1.2 Binary attributes .................................................................................. 2.1.3 Ordinal attributes................................................................................... 2.1.4 Numeric attributes................................................................................. 2.1.5 Discrete vs. continuous attributes.......................................................... Statistics of data .............................................................................................. 2.2.1 Measuring the central tendency............................................................ 2.2.2 Measuring the dispersion of data.......................................................... 23 24 24 25 25 26 27 27 28 31 vii
viii Contents 2.2.3 Covariance and correlation analysis..................................................... 2.2.4 Graphic displays of basic statistics of data.......................................... Similarity and distance measures...................................................................... 2.3.1 Data matrix vs. dissimilarity matrix..................................................... 2.3.2 Proximity measures for nominal attributes........................................... 2.3.3 Proximity measures for binary attributes ............................................. 2.3.4 Dissimilarity of numeric data: Minkowski distance ............................. 2.3.5 Proximity measures for ordinal attributes............................................. 2.3.6 Dissimilarity for attributes of mixed types ........................................... 2.3.7 Cosine similarity.................................................................................. 2.3.8 Measuring similar distributions: the Kullback-Leibler divergence .... 2.3.9 Capturing hidden semantics insimilarity measures................................ Data quality, data cleaning, and dataintegration................................................ 2.4.1 Data quality measures........................................................................... 2.4.2 Data cleaning ........................................................................................ 2.4.3 Data integration..................................................................................... Data
transformation........................................................................................... 2.5.1 Normalization........................................................................................ 2.5.2 Discretization ........................................................................................ 2.5.3 Data compression ................................................................................. 2.5.4 Sampling.......................................................................... Dimensionality reduction................................................................................... 2.6.1 Principal components analysis.............................................................. 2.6.2 Attribute subset selection....................................................................... 2.6.3 Nonlinear dimensionality reductionmethods........................................ Summary............................................................................................................ Exercises............................................................................................................ Bibliographic notes........................................................................................... 34 38 43 43 44 46 48 49 50 52 53 55 55 55 56 62 63 64 65 68 70 71 71 72 74 79 80 83 Data warehousing and online analyticalprocessing........................................... 85 Datawarehouse.................................................................................................. 3.1.1 Data warehouse: what and why?
.......................................................... 3.1.2 Architecture of data warehouses: enterprise data warehouses and data marts......................................................................................... 88 3.1.3 Data lakes.............................................................................................. 3.2 Data warehouse modeling: schema andmeasures.............................................. 3.2.1 Data cube: a multidimensional data model........................................... 3.2.2 Schemas for multidimensional data models: stars, snowflakes, and fact constellations............................................................................ 99 3.2.3 Concept hierarchies............................................................................... 3.2.4 Measures: categorization and computation........................................... 3.3 OLAP operations............................................................................................... 3.3.1 Typical OLAP operations....................................................................... 3.3.2 Indexing OLAP data: bitmap index and join index............................... 3.3.3 Storage implementation: column-based databases ............................... 85 85 2.3 2.4 2.5 2.6 2.7 2.8 2.9 CHAPTER 3 3.1 93 96 97 103 105 106 106 108 Ill
Contents 3.4 3.5 3.6 3.7 3.8 CHAPTER 4 4.1 4.2 4.3 4.4 4.5 4.6 CHAPTER 5 5.1 5.2 ix Data cube computation .................................................................................... 3.4.1 Terminology of data cube computation ............................................... 3.4.2 Data cube materialization: ideas........................................................... 3.4.3 OLAP server architectures: ROLAP vs. MOLAP vs. HOLAP ........... 3.4.4 General strategies for data cube computation...................................... Data cube computation methods....................................................................... 3.5.1 Multiway array aggregation for full cube computation........................ 3.5.2 BUC: computing iceberg cubes from the apex cuboid downward .... 3.5.3 Precomputing shell fragments for fast high-dimensional OLAP......... 3.5.4 Efficient processing of OLAP queries using cuboids............................ Summary........................................................................................................... Exercises........................................................................................................... Bibliographic notes.......................................................................................... ИЗ 113 115 117 119 120 121 125 129 132 133 135 142 Pattern mining: basic concepts and methods ....................................................... 145 Basic concepts.................................................................................................. 4.1.1 Market basket analysis: a
motivating example .................................... 4.1.2 Frequent itemsets, closed itemsets, and association rules .................... Frequent itemset mining methods..................................................................... 4.2.1 Apriori algorithm: finding frequent itemsets by confined candidate generation................................................................................ 150 4.2.2 Generating association rules from frequent itemsets............................ 4.2.3 Improving the efficiency of Apriori ..................................................... 4.2.4 A pattern-growth approach for mining frequent itemsets .................... 4.2.5 Mining frequent itemsets using the vertical data format ...................... 4.2.6 Mining closed and max patterns............................................................ Which patterns are interesting?—Pattern evaluation methods......................... 4.3.1 Strong rules are not necessarily interesting........................................... 4.3.2 From association analysis to correlation analysis ................................ 4.3.3 A comparison of pattern evaluation measures....................................... Summary........................................................................................................... Exercises........................................................................................................... Bibliographic notes.......................................................................................... 145 145 147 149 153 155 157 160 162 163 163
164 165 169 170 173 Pattern mining: advanced methods.......................................................................... 175 Mining various kinds of patterns ..................................................................... 5.1.1 Mining multilevel associations.............................................................. 5.1.2 Mining multidimensional associations................................................. 5.1.3 Mining quantitative association rules................................................... 5.1.4 Mining high-dimensional data.............................................................. 5.1.5 Mining rare patterns and negative patterns........................................... Mining compressed or approximate patterns.................................................... 5.2.1 Mining compressed patterns by pattern clustering .............................. 5.2.2 Extracting redundancy-aware top-к patterns........................................ 175 175 179 180 183 185 187 187 189
x Contents 5.3 Constraint-based pattern mining......................................................................... 5.4 5.5 5.6 5.7 5.8 5.9 CHAPTER 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 5.3.1 Pruning pattern space with pattern pruning constraints......................... 5.3.2 Pruning data space with data pruning constraints................................. 5.3.3 Mining space pruning with succinctness constraints............................. Mining sequential patterns................................................................................. 5.4.1 Sequential pattern mining: concepts and primitives ............................. 5.4.2 Scalable methods for mining sequential patterns ................................. 5.4.3 Constraint-based mining of sequential patterns..................................... Mining subgraph patterns................................................................................... 5.5.1 Methods for mining frequent subgraphs .. ;......................................... 5.5.2 Mining variant and constrained substructure patterns........................... Pattern mining: application examples................................................................ 5.6.1 Phrase mining in massive text data........................................................ 5.6.2 Mining copy and paste bugs in software programs............................... Summary............................................................................................................
Exercises............................................................................................................ Bibliographic notes........................................................................................... 191 193 196 197 198 198 200 210 211 212 219 223 223 230 232 233 235 Classification: basic concepts and methods ....................................................... 239 Basic concepts.................................................................................................... 6.1.1 What is classification?........................................................................... 6.1.2 General approach to classification ....................................................... Decision tree induction ..................................................................................... 6.2.1 Decision tree induction ......................................................................... 6.2.2 Attribute selection measures ................................................................. 6.2.3 Tree pruning.......................................................................................... Bayes classification methods............................................................................. 6.3.1 Bayes’ theorem...................................................................................... 6.3.2 Naïve Bayesian classification................................................................. Lazy learners (or learning from your neighbors)............................................... 6.4.1 Æ-nearest-neighbor
classifiers................................................................ 6.4.2 Case-based reasoning ........................................................................... Linear classifiers ...................................................................................... . ... . 6.5.1 Linear regression.................................................................................... 6.5.2 Perceptron: turning linear regression to classification........................... 6.5.3 Logistic regression ............................................................................... Model evaluation and selection ........................................................................ 6.6.1 Metrics for evaluating classifier performance....................................... 6.6.2 Holdout method and random subsampling............................................ 6.6.3 Cross-validation .................................................................................... 6.6.4 Bootstrap................................................................................................ 6.6.5 Model selection using statistical tests of significance........................... 6.6.6 Comparing classifiers based on cost-benefit andROC curves............... Techniques to improve classification accuracy ................................................. 6.7.1 Introducing ensemble methods............................................................... 239 239 240 243 244 248 257 259 260 262 266 266 269 269 270 272 274 278 278 283 283 284 285 286 290 290
Contents xi 6.7.2 Bagging.................................................................................................. 6.7.3 Boosting................................................................................................ 6.7.4 Random forests...................................................................................... 6.7.5 Improving classification accuracy of class-imbalanced data ................ 6.8 Summary............................................................................................................ 6.9 Exercises............................................................................................................ 6.10 Bibliographic notes ........................................................................................... 291 292 296 297 298 299 302 Classification: advanced methods......................................................................... 307 Feature selection and engineering .................................................................... 7.1.1 Filter methods........................................................................................ 7.1.2 Wrapper methods ................................................................................. 7.1.3 Embedded methods............................................................................... Bayesian belief networks................................................................................... 7.2.1 Concepts and mechanisms..................................................................... 7.2.2 Training Bayesian belief
networks........................................................ Support vector machines ................................................................................... 7.3.1 Linear support vector machines............................................................ 7.3.2 Nonlinear support vector machines ...................................................... Rule-based and pattern-based classification ..................................................... 7.4.1 Using IF-THEN rules for classification ................................................ 7.4.2 Rule extraction from a decision tree...................................................... 7.4.3 Rule induction using a sequential covering algorithm........................... 7.4.4 Associative classification....................................................................... 7.4.5 Discriminative frequent pattern-based classification............................. Classification with weak supervision................................................................ 7.5.1 Semisupervised classification................................................................. 7.5.2 Active learning ...................................................................................... 7.5.3 Transfer learning................................................................................... 7.5.4 Distant supervision ............................................................................... 7.5.5 Zero-shot learning................................................................................. Classification with rich data
type...................................................................... 7.6.1 Stream data classification....................................................................... 7.6.2 Sequence classification ......................................................................... 7.6.3 Graph data classification ....................................................................... Potpourri: other related techniques.................................................................... 7.7.1 Multiclass classification......................................................................... 7.7.2 Distance metric learning ....................................................................... 7.7.3 Interpretability of classification............................................................ 7.7.4 Genetic algorithms ............................................................................... 7.7.5 Reinforcement learning......................................................................... Summary............................................................................................................ Exercises............................................................................................................ Bibliographic notes ........................................................................................... 307 308 311 312 315 315 317 318 319 324 327 328 330 331 335 338 342 343 345 346 348 349 351 352 354 355 359 359 362 364 367 367 369 370 374 CHAPTER 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10
xii Contents CHAPTER 8 Cluster analysis: basic concepts and methods ...................................................... 379 Cluster analysis.................................................................................................. 8.1.1 What is cluster analysis?................................................... 8.1.2 Requirements for cluster analysis.......................................................... 8.1.3 Overview of basic clustering methods ................................................. Partitioning methods .......................................................................................... 8.2.1 £-Means: a centroid-based technique................................................... 8.2.2 Variations of ¿-means .......................................................................... Hierarchical methods.......................................................................................... 8.3.1 Basic concepts of hierarchical clustering ............................................. 8.3.2 Agglomerative hierarchical clustering ................................................. 8.3.3 Divisive hierarchical clustering ........................................................... 8.3.4 BIRCH: scalable hierarchical clustering using clustering feature trees 8.3.5 Probabilistic hierarchical clustering..................................................... Density-based and grid-based methods.............................................................. 8.4.1 DB SCAN: density-based clustering based on connected regions with high
density........................................................................................... 8.4.2 DENCLUE: clustering based on density distributionfunctions............ 8.4.3 Grid-based methods.............................................................................. Evaluation of clustering.................................................................................... 8.5.1 Assessing clustering tendency............................................................. 8.5.2 Determining the number of clusters...................................................... 8.5.3 Measuring clustering quality: extrinsic methods................................... 8.5.4 Intrinsic methods................................................................................... Summary.......................... Exercises........................................................................................................... Bibliographic notes....................................... 379 380 381 383 385 386 388 394 394 397 400 402 404 407 Cluster analysis: advanced methods........................................................................ 431 Probabilistic model-based clustering ............................................................... 9.1.1 Fuzzy clusters....................................................................................... 9.1.2 Probabilistic model-based clusters........................................................ 9.1.3 Expectation-maximization algorithm................................................... 9.2 Clustering high-dimensional
data..................................................................... 9.2.1 Why is clustering high-dimensional data challenging?........................ 9.2.2 Axis-parallel subspace approaches........................................................ 9.2.3 Arbitrarily oriented subspace approaches............................................ 9.3 Biclustering ....................................................................................................... 9.3.1 Why and where is biclustering useful?................................................. 9.3.2 Types of biclusters................................................................................ 9.3.3 Biclustering methods............................................................................ 9.3.4 Enumerating all biclusters using MaPle............................................... 9.4 Dimensionality reduction for clustering.......................................................... 9.4.1 Linear dimensionality reduction methods for clustering...................... 9.4.2 Nonnegative matrix factorization (NMF)............................................ 431 433 435 438 441 441 445 447 447 448 450 452 453 454 455 458 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 CHAPTER 9 9.1 408 411 414 417 417 419 420 424 425 427 429
Contents 9.4.3 Spectral clustering................................................................................. Clustering graph and network data................................................................... 9.5.1 Applications and challenges.................................................................. 9.5.2 Similarity measures............................................................................... 9.5.3 Graph clustering methods .................................................................... 9.6 Semisupervised clustering............................................................................... 9.6.1 Semisupervised clustering on partially labeled data ............................ 9.6.2 Semisupervised clustering on pairwise constraints.............................. 9.6.3 Other types of background knowledge for semisupervised clustering . 9.7 Summary........................................................................................................... 9.8 Exercises........................................................................................................... 9.9 Bibliographic notes.......................................................................................... 9.5 xiii 460 463 463 465 470 475 475 476 477 479 480 482 CHAPTER 10 Deep learning......................................................................................................... 485 10.1 Basic concepts.................................................................................................. 10.1.1 What is deep
learning?.......................................................................... 10.1.2 Backpropagation algorithm................................................................. 10.1.3 Key challenges for training deep learning models................................ 10.1.4 Overview of deep learning architecture ............................................... Improve training of deep learning models........................................................ 10.2.1 Responsive activation functions........................................................... 10.2.2 Adaptive learning rate.......................................................................... 10.2.3 Dropout................................................................................................. 10.2.4 Pretraining............................................................................................. 10.2.5 Cross-entropy ...................................................................................... 10.2.6 Autoencoder: unsupervised deep learning .......................................... 10.2.7 Other techniques.................................................................................. Convolutional neural networks......................................................................... 10.3.1 Introducing convolution operation....................................................... 10.3.2 Multidimensional convolution............................................................. 10.3.3 Convolutional layer..............................................................................
Recurrent neural networks............................................................................... 10.4.1 Basic RNN models and applications ................................................... 10.4.2 Gated RNNs ........................................................................................ 10.4.3 Other techniques for addressing long-term dependence ..................... Graph neural networks...................................................................................... 10.5.1 Basic concepts...................................................................................... 10.5.2 Graph convolutional networks............................................................. 10.5.3 Other types of GNNs............................................................................ Summary........................................................................................................... Exercises........................................................................................................... Bibliographic notes .......................................................................................... 485 485 489 498 499 500 500 501 504 507 509 511 514 517 517 519 523 526 526 532 536 539 540 541 545 547 548 552 Outlier detection..................................................................................................... 557 Basic concepts................................................................................................... 557 10.2 10.3 10.4 10.5 10.6 10.7 10.8 CHAPTER 11 11.1
xiv Contents 11.1.1 What are outliers?................................................................................ 11.1.2 Types of outliers .................................................................................. 11.1.3 Challenges of outlier detection............................................................. 11.1.4 An overview of outlier detection methods .......................................... Statistical approaches......................................................................................... 11.2.1 Parametric methods............................................................................. 11.2.2 Nonparametric methods....................................................................... Proximity-based approaches ............................................................................ 11.3.1 Distance-based outlier detection.......................................................... 11.3.2 Density-based outlier detection .......................................................... Reconstruction-based approaches...................................................................... 11.4.1 Matrix factorization-based methodsfor numerical data ...................... 11.4.2 Pattern-based compression methods for categoricaldata...................... Clustering- vs. classification-based approaches................................................. 11.5.1 Clustering-based approaches.............................................................. 11.5.2 Classification-based approaches.......................................................... Mining
contextual and collective outliers......................................................... 11.6.1 Transforming contextual outlier detection to conventional outlier detection................................................................................... 591 11.6.2 Modeling normal behavior with respect tocontexts ............................ 11.6.3 Mining collective outliers ........................................................ Outlier detection in high-dimensional data....................................................... 11.7.1 Extending conventional outlier detection........................................... 11.7.2 Finding outliers in subspaces.............................................................. 11.7.3 Outlier detection ensemble................................................................... 11.7.4 Taming high dimensionality by deep learning..................................... 11.7.5 Modeling high-dimensionaloutliers...................................................... Summary............................................................................................................ Exercises............................................................................................................ Bibliographic notes........................................................................................... 558 559 561 562 565 565 569 572 572 573 576 577 582 585 585 588 590 Data mining trends and research frontiers........................................................... 605 Mining rich data
types....................................................................................... 12.1.1 Mining text data ................................................................................. 12.1.2 Spatial-temporal data............................................................................ 12.1.3 Graph and networks.............................................................................. 12.2 Data mining applications................................................................................... 12.2.1 Data mining for sentiment and opinion................................................ 12.2.2 Truth discovery and misinformation identification............................. 12.2.3 Information and disease propagation .................................................. 12.2.4 Productivity and team science ............................................................ 12.3 Dataminingmethodologiesandsystems ......................................................... 12.3.1 Structuring unstructured data for knowledge mining: a data-driven approach................................................................................... 629 12.3.2 Data augmentation............................................................................... 605 605 610 612 617 617 620 623 626 629 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 11.10 CHAPTER 12 12.1 591 592 593 594 595 596 597 599 600 601 602 632
Contents XV 12.3.3 From correlation to causality............................................................... 12.3.4 Network as a context............................................................................ 12.3.5 Auto-ML: methods and systems.......................................................... 12.4 Data mining, people, and society..................................................................... 12.4.1 Privacy-preserving data mining .......................................................... 12.4.2 Human-algorithm interaction.............................................................. 12.4.3 Mining beyond maximizing accuracy: fairness, interpretability, and robustness................................................................................ 648 12.4.4 Data mining for social good.................................................................. 635 637 640 642 642 646 Mathematical background.......................................................................................... 655 Probability and statistics .................................................................................. A. 1.1 PDF of typical distributions.................................................................. A. 1.2 MLEandMAP..................................................................................... A. 1.3 Significance test ................................................................................... A. 1.4 Density estimation................................................................................. A. 1.5 Bias-variance
tradeoff.......................................................................... A. 1.6 Cross-validation and Jackknife.............................................................. Numerical optimization.................................................................................... A.2.1 Gradient descent.................................................................................. A.2.2 Variants of gradient descent.................................................................. A.2.3 Newton’s method................................................................................... A.2.4 Coordinate descent ............................................................................... A.2.5 Quadratic programming........................................................................ Matrix and linear algebra.................................................................................. A.3.1 Linear system Ax = b..................................................................................... A.3.2 Norms of vectors and matrices.............................................................. A.3.3 Matrix decompositions ........................................................................ A.3.4 Subspace................................................................................................ A.3.5 Orthogonality ....................................................................................... Concepts and tools from signal processing...................................................... A.4.1
Entropy.................................................................................................. A.4.2 Kullback-Leibler divergence (KL-divergence) ..................................... A.4.3 Mutual information............................................................................... A.4.4 Discrete Fourier transform (DFT) and fast Fourier transform (FFT) . . Bibliographic notes .......................................................................................... 655 655 656 657 658 659 660 661 661 662 664 666 666 668 668 669 669 671 672 673 673 674 675 676 678 APPENDIX A A.1 A.2 A.3 A.4 A.5 Bibliography Index ........ 652 681 735
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| adam_txt |
Contents Foreword . Foreword to second edition. Preface. Acknowledgments . About the authors. CHAPTER 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 CHAPTER 2 2.1 2.2 xvii xix xxi xxvii xxix Introduction. What is data mining?. Data mining: an essential step in knowledge discovery. Diversity of data types for data mining. Mining various kinds of knowledge. 1.4.1 Multidimensional data summarization. 1.4.2 Mining frequent patterns, associations, and correlations. 1.4.3 Classification and regression for predictive analysis. 1.4.4 Cluster
analysis. 1.4.5 Deep learning . 1.4.6 Outlier analysis. 1.4.7 Are all mining results interesting? . Data mining: confluence of multiple disciplines. 1.5.1 Statistics and data mining . 1.5.2 Machine learning and data mining. 1.5.3 Database technology and data mining . 1.5.4 Data mining and data science. 1.5.5 Data mining and other disciplines . Data mining and applications. Data mining and society. Summary. Exercises. Bibliographic notes. 1 1 2 4 5 6 6 7 9 9 10 10 12 12 13 15 15 16 17 19 19 20 21
Data, measurements, and data preprocessing. Datatypes. 2.1.1 Nominal attributes. 2.1.2 Binary attributes . 2.1.3 Ordinal attributes. 2.1.4 Numeric attributes. 2.1.5 Discrete vs. continuous attributes. Statistics of data . 2.2.1 Measuring the central tendency. 2.2.2 Measuring the dispersion of data. 23 24 24 25 25 26 27 27 28 31 vii
viii Contents 2.2.3 Covariance and correlation analysis. 2.2.4 Graphic displays of basic statistics of data. Similarity and distance measures. 2.3.1 Data matrix vs. dissimilarity matrix. 2.3.2 Proximity measures for nominal attributes. 2.3.3 Proximity measures for binary attributes . 2.3.4 Dissimilarity of numeric data: Minkowski distance . 2.3.5 Proximity measures for ordinal attributes. 2.3.6 Dissimilarity for attributes of mixed types . 2.3.7 Cosine similarity. 2.3.8 Measuring similar distributions: the Kullback-Leibler divergence . 2.3.9 Capturing hidden semantics insimilarity measures. Data quality, data cleaning, and dataintegration. 2.4.1 Data quality measures. 2.4.2 Data cleaning . 2.4.3 Data integration. Data
transformation. 2.5.1 Normalization. 2.5.2 Discretization . 2.5.3 Data compression . 2.5.4 Sampling. Dimensionality reduction. 2.6.1 Principal components analysis. 2.6.2 Attribute subset selection. 2.6.3 Nonlinear dimensionality reductionmethods. Summary. Exercises. Bibliographic notes. 34 38 43 43 44 46 48 49 50 52 53 55 55 55 56 62 63 64 65 68 70 71 71 72 74 79 80 83 Data warehousing and online analyticalprocessing. 85 Datawarehouse. 3.1.1 Data warehouse: what and why?
. 3.1.2 Architecture of data warehouses: enterprise data warehouses and data marts. 88 3.1.3 Data lakes. 3.2 Data warehouse modeling: schema andmeasures. 3.2.1 Data cube: a multidimensional data model. 3.2.2 Schemas for multidimensional data models: stars, snowflakes, and fact constellations. 99 3.2.3 Concept hierarchies. 3.2.4 Measures: categorization and computation. 3.3 OLAP operations. 3.3.1 Typical OLAP operations. 3.3.2 Indexing OLAP data: bitmap index and join index. 3.3.3 Storage implementation: column-based databases . 85 85 2.3 2.4 2.5 2.6 2.7 2.8 2.9 CHAPTER 3 3.1 93 96 97 103 105 106 106 108 Ill
Contents 3.4 3.5 3.6 3.7 3.8 CHAPTER 4 4.1 4.2 4.3 4.4 4.5 4.6 CHAPTER 5 5.1 5.2 ix Data cube computation . 3.4.1 Terminology of data cube computation . 3.4.2 Data cube materialization: ideas. 3.4.3 OLAP server architectures: ROLAP vs. MOLAP vs. HOLAP . 3.4.4 General strategies for data cube computation. Data cube computation methods. 3.5.1 Multiway array aggregation for full cube computation. 3.5.2 BUC: computing iceberg cubes from the apex cuboid downward . 3.5.3 Precomputing shell fragments for fast high-dimensional OLAP. 3.5.4 Efficient processing of OLAP queries using cuboids. Summary. Exercises. Bibliographic notes. ИЗ 113 115 117 119 120 121 125 129 132 133 135 142 Pattern mining: basic concepts and methods . 145 Basic concepts. 4.1.1 Market basket analysis: a
motivating example . 4.1.2 Frequent itemsets, closed itemsets, and association rules . Frequent itemset mining methods. 4.2.1 Apriori algorithm: finding frequent itemsets by confined candidate generation. 150 4.2.2 Generating association rules from frequent itemsets. 4.2.3 Improving the efficiency of Apriori . 4.2.4 A pattern-growth approach for mining frequent itemsets . 4.2.5 Mining frequent itemsets using the vertical data format . 4.2.6 Mining closed and max patterns. Which patterns are interesting?—Pattern evaluation methods. 4.3.1 Strong rules are not necessarily interesting. 4.3.2 From association analysis to correlation analysis . 4.3.3 A comparison of pattern evaluation measures. Summary. Exercises. Bibliographic notes. 145 145 147 149 153 155 157 160 162 163 163
164 165 169 170 173 Pattern mining: advanced methods. 175 Mining various kinds of patterns . 5.1.1 Mining multilevel associations. 5.1.2 Mining multidimensional associations. 5.1.3 Mining quantitative association rules. 5.1.4 Mining high-dimensional data. 5.1.5 Mining rare patterns and negative patterns. Mining compressed or approximate patterns. 5.2.1 Mining compressed patterns by pattern clustering . 5.2.2 Extracting redundancy-aware top-к patterns. 175 175 179 180 183 185 187 187 189
x Contents 5.3 Constraint-based pattern mining. 5.4 5.5 5.6 5.7 5.8 5.9 CHAPTER 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 5.3.1 Pruning pattern space with pattern pruning constraints. 5.3.2 Pruning data space with data pruning constraints. 5.3.3 Mining space pruning with succinctness constraints. Mining sequential patterns. 5.4.1 Sequential pattern mining: concepts and primitives . 5.4.2 Scalable methods for mining sequential patterns . 5.4.3 Constraint-based mining of sequential patterns. Mining subgraph patterns. 5.5.1 Methods for mining frequent subgraphs . ;. 5.5.2 Mining variant and constrained substructure patterns. Pattern mining: application examples. 5.6.1 Phrase mining in massive text data. 5.6.2 Mining copy and paste bugs in software programs. Summary.
Exercises. Bibliographic notes. 191 193 196 197 198 198 200 210 211 212 219 223 223 230 232 233 235 Classification: basic concepts and methods . 239 Basic concepts. 6.1.1 What is classification?. 6.1.2 General approach to classification . Decision tree induction . 6.2.1 Decision tree induction . 6.2.2 Attribute selection measures . 6.2.3 Tree pruning. Bayes classification methods. 6.3.1 Bayes’ theorem. 6.3.2 Naïve Bayesian classification. Lazy learners (or learning from your neighbors). 6.4.1 Æ-nearest-neighbor
classifiers. 6.4.2 Case-based reasoning . Linear classifiers . . . . 6.5.1 Linear regression. 6.5.2 Perceptron: turning linear regression to classification. 6.5.3 Logistic regression . Model evaluation and selection . 6.6.1 Metrics for evaluating classifier performance. 6.6.2 Holdout method and random subsampling. 6.6.3 Cross-validation . 6.6.4 Bootstrap. 6.6.5 Model selection using statistical tests of significance. 6.6.6 Comparing classifiers based on cost-benefit andROC curves. Techniques to improve classification accuracy . 6.7.1 Introducing ensemble methods. 239 239 240 243 244 248 257 259 260 262 266 266 269 269 270 272 274 278 278 283 283 284 285 286 290 290
Contents xi 6.7.2 Bagging. 6.7.3 Boosting. 6.7.4 Random forests. 6.7.5 Improving classification accuracy of class-imbalanced data . 6.8 Summary. 6.9 Exercises. 6.10 Bibliographic notes . 291 292 296 297 298 299 302 Classification: advanced methods. 307 Feature selection and engineering . 7.1.1 Filter methods. 7.1.2 Wrapper methods . 7.1.3 Embedded methods. Bayesian belief networks. 7.2.1 Concepts and mechanisms. 7.2.2 Training Bayesian belief
networks. Support vector machines . 7.3.1 Linear support vector machines. 7.3.2 Nonlinear support vector machines . Rule-based and pattern-based classification . 7.4.1 Using IF-THEN rules for classification . 7.4.2 Rule extraction from a decision tree. 7.4.3 Rule induction using a sequential covering algorithm. 7.4.4 Associative classification. 7.4.5 Discriminative frequent pattern-based classification. Classification with weak supervision. 7.5.1 Semisupervised classification. 7.5.2 Active learning . 7.5.3 Transfer learning. 7.5.4 Distant supervision . 7.5.5 Zero-shot learning. Classification with rich data
type. 7.6.1 Stream data classification. 7.6.2 Sequence classification . 7.6.3 Graph data classification . Potpourri: other related techniques. 7.7.1 Multiclass classification. 7.7.2 Distance metric learning . 7.7.3 Interpretability of classification. 7.7.4 Genetic algorithms . 7.7.5 Reinforcement learning. Summary. Exercises. Bibliographic notes . 307 308 311 312 315 315 317 318 319 324 327 328 330 331 335 338 342 343 345 346 348 349 351 352 354 355 359 359 362 364 367 367 369 370 374 CHAPTER 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10
xii Contents CHAPTER 8 Cluster analysis: basic concepts and methods . 379 Cluster analysis. 8.1.1 What is cluster analysis?. 8.1.2 Requirements for cluster analysis. 8.1.3 Overview of basic clustering methods . Partitioning methods . 8.2.1 £-Means: a centroid-based technique. 8.2.2 Variations of ¿-means . Hierarchical methods. 8.3.1 Basic concepts of hierarchical clustering . 8.3.2 Agglomerative hierarchical clustering . 8.3.3 Divisive hierarchical clustering . 8.3.4 BIRCH: scalable hierarchical clustering using clustering feature trees 8.3.5 Probabilistic hierarchical clustering. Density-based and grid-based methods. 8.4.1 DB SCAN: density-based clustering based on connected regions with high
density. 8.4.2 DENCLUE: clustering based on density distributionfunctions. 8.4.3 Grid-based methods. Evaluation of clustering. 8.5.1 Assessing clustering tendency. 8.5.2 Determining the number of clusters. 8.5.3 Measuring clustering quality: extrinsic methods. 8.5.4 Intrinsic methods. Summary. Exercises. Bibliographic notes. 379 380 381 383 385 386 388 394 394 397 400 402 404 407 Cluster analysis: advanced methods. 431 Probabilistic model-based clustering . 9.1.1 Fuzzy clusters. 9.1.2 Probabilistic model-based clusters. 9.1.3 Expectation-maximization algorithm. 9.2 Clustering high-dimensional
data. 9.2.1 Why is clustering high-dimensional data challenging?. 9.2.2 Axis-parallel subspace approaches. 9.2.3 Arbitrarily oriented subspace approaches. 9.3 Biclustering . 9.3.1 Why and where is biclustering useful?. 9.3.2 Types of biclusters. 9.3.3 Biclustering methods. 9.3.4 Enumerating all biclusters using MaPle. 9.4 Dimensionality reduction for clustering. 9.4.1 Linear dimensionality reduction methods for clustering. 9.4.2 Nonnegative matrix factorization (NMF). 431 433 435 438 441 441 445 447 447 448 450 452 453 454 455 458 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 CHAPTER 9 9.1 408 411 414 417 417 419 420 424 425 427 429
Contents 9.4.3 Spectral clustering. Clustering graph and network data. 9.5.1 Applications and challenges. 9.5.2 Similarity measures. 9.5.3 Graph clustering methods . 9.6 Semisupervised clustering. 9.6.1 Semisupervised clustering on partially labeled data . 9.6.2 Semisupervised clustering on pairwise constraints. 9.6.3 Other types of background knowledge for semisupervised clustering . 9.7 Summary. 9.8 Exercises. 9.9 Bibliographic notes. 9.5 xiii 460 463 463 465 470 475 475 476 477 479 480 482 CHAPTER 10 Deep learning. 485 10.1 Basic concepts. 10.1.1 What is deep
learning?. 10.1.2 Backpropagation algorithm. 10.1.3 Key challenges for training deep learning models. 10.1.4 Overview of deep learning architecture . Improve training of deep learning models. 10.2.1 Responsive activation functions. 10.2.2 Adaptive learning rate. 10.2.3 Dropout. 10.2.4 Pretraining. 10.2.5 Cross-entropy . 10.2.6 Autoencoder: unsupervised deep learning . 10.2.7 Other techniques. Convolutional neural networks. 10.3.1 Introducing convolution operation. 10.3.2 Multidimensional convolution. 10.3.3 Convolutional layer.
Recurrent neural networks. 10.4.1 Basic RNN models and applications . 10.4.2 Gated RNNs . 10.4.3 Other techniques for addressing long-term dependence . Graph neural networks. 10.5.1 Basic concepts. 10.5.2 Graph convolutional networks. 10.5.3 Other types of GNNs. Summary. Exercises. Bibliographic notes . 485 485 489 498 499 500 500 501 504 507 509 511 514 517 517 519 523 526 526 532 536 539 540 541 545 547 548 552 Outlier detection. 557 Basic concepts. 557 10.2 10.3 10.4 10.5 10.6 10.7 10.8 CHAPTER 11 11.1
xiv Contents 11.1.1 What are outliers?. 11.1.2 Types of outliers . 11.1.3 Challenges of outlier detection. 11.1.4 An overview of outlier detection methods . Statistical approaches. 11.2.1 Parametric methods. 11.2.2 Nonparametric methods. Proximity-based approaches . 11.3.1 Distance-based outlier detection. 11.3.2 Density-based outlier detection . Reconstruction-based approaches. 11.4.1 Matrix factorization-based methodsfor numerical data . 11.4.2 Pattern-based compression methods for categoricaldata. Clustering- vs. classification-based approaches. 11.5.1 Clustering-based approaches. 11.5.2 Classification-based approaches. Mining
contextual and collective outliers. 11.6.1 Transforming contextual outlier detection to conventional outlier detection. 591 11.6.2 Modeling normal behavior with respect tocontexts . 11.6.3 Mining collective outliers . Outlier detection in high-dimensional data. 11.7.1 Extending conventional outlier detection. 11.7.2 Finding outliers in subspaces. 11.7.3 Outlier detection ensemble. 11.7.4 Taming high dimensionality by deep learning. 11.7.5 Modeling high-dimensionaloutliers. Summary. Exercises. Bibliographic notes. 558 559 561 562 565 565 569 572 572 573 576 577 582 585 585 588 590 Data mining trends and research frontiers. 605 Mining rich data
types. 12.1.1 Mining text data . 12.1.2 Spatial-temporal data. 12.1.3 Graph and networks. 12.2 Data mining applications. 12.2.1 Data mining for sentiment and opinion. 12.2.2 Truth discovery and misinformation identification. 12.2.3 Information and disease propagation . 12.2.4 Productivity and team science . 12.3 Dataminingmethodologiesandsystems . 12.3.1 Structuring unstructured data for knowledge mining: a data-driven approach. 629 12.3.2 Data augmentation. 605 605 610 612 617 617 620 623 626 629 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 11.10 CHAPTER 12 12.1 591 592 593 594 595 596 597 599 600 601 602 632
Contents XV 12.3.3 From correlation to causality. 12.3.4 Network as a context. 12.3.5 Auto-ML: methods and systems. 12.4 Data mining, people, and society. 12.4.1 Privacy-preserving data mining . 12.4.2 Human-algorithm interaction. 12.4.3 Mining beyond maximizing accuracy: fairness, interpretability, and robustness. 648 12.4.4 Data mining for social good. 635 637 640 642 642 646 Mathematical background. 655 Probability and statistics . A. 1.1 PDF of typical distributions. A. 1.2 MLEandMAP. A. 1.3 Significance test . A. 1.4 Density estimation. A. 1.5 Bias-variance
tradeoff. A. 1.6 Cross-validation and Jackknife. Numerical optimization. A.2.1 Gradient descent. A.2.2 Variants of gradient descent. A.2.3 Newton’s method. A.2.4 Coordinate descent . A.2.5 Quadratic programming. Matrix and linear algebra. A.3.1 Linear system Ax = b. A.3.2 Norms of vectors and matrices. A.3.3 Matrix decompositions . A.3.4 Subspace. A.3.5 Orthogonality . Concepts and tools from signal processing. A.4.1
Entropy. A.4.2 Kullback-Leibler divergence (KL-divergence) . A.4.3 Mutual information. A.4.4 Discrete Fourier transform (DFT) and fast Fourier transform (FFT) . . Bibliographic notes . 655 655 656 657 658 659 660 661 661 662 664 666 666 668 668 669 669 671 672 673 673 674 675 676 678 APPENDIX A A.1 A.2 A.3 A.4 A.5 Bibliography Index . 652 681 735 |
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| author | Han, Jiawei 1949- Pei, Jian Tong, Hanghang ca. 20./21. Jh |
| author_GND | (DE-588)137798342 (DE-588)1021437980 (DE-588)135615844 |
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| edition | fourth edition |
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| illustrated | Illustrated |
| index_date | 2024-07-03T19:48:00Z |
| indexdate | 2024-07-10T09:31:59Z |
| institution | BVB |
| isbn | 9780128117606 |
| language | English |
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| spelling | Han, Jiawei 1949- Verfasser (DE-588)137798342 aut Data mining concepts and techniques Jiawei Han ; Hanghang tong ; Jian Pei fourth edition Amsterdam [u.a.] Morgan Kaufmann Publishers 2023 xxix, 752 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Pei, Jian Verfasser (DE-588)1021437980 aut Tong, Hanghang ca. 20./21. Jh. Verfasser (DE-588)135615844 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033587630&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Han, Jiawei 1949- Pei, Jian Tong, Hanghang ca. 20./21. Jh Data mining concepts and techniques Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Data mining concepts and techniques |
| title_auth | Data mining concepts and techniques |
| title_exact_search | Data mining concepts and techniques |
| title_exact_search_txtP | Data mining concepts and techniques |
| title_full | Data mining concepts and techniques Jiawei Han ; Hanghang tong ; Jian Pei |
| title_fullStr | Data mining concepts and techniques Jiawei Han ; Hanghang tong ; Jian Pei |
| title_full_unstemmed | Data mining concepts and techniques Jiawei Han ; Hanghang tong ; Jian Pei |
| title_short | Data mining |
| title_sort | data mining concepts and techniques |
| title_sub | concepts and techniques |
| topic | Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033587630&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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