Introduction to data mining:
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Pearson
[2020]
|
| Ausgabe: | Second edition, global edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Bereits 2019 im Buchhandel erhältlich. - Impressum: "Authorized adaptation from the United States edition, entitled Introduction to Data Mining, 2nd Edition, ISBN 978-0-13-312890-1" |
| Beschreibung: | 859 Seiten Diagramme |
| ISBN: | 9780273769224 0133128903 |
Internformat
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| 245 | 1 | 0 | |a Introduction to data mining |c Pang-Ning Tan (Michigan State University), Michael Steinbach (University of Minnesota), Anuj Karpatne (University of Minnesota), Vipin Kumar (University of Minnesota) |
| 250 | |a Second edition, global edition | ||
| 264 | 1 | |a New York |b Pearson |c [2020] | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Preface to the Second Edition 5 1 Introduction 1.1 What Is Data Mining?.................................................... 1.2 Motivating Challenges.................................................................... 1.3 The Origins of Data Mining.................................... 1.4 Data Mining Tasks................................................................... 1.5 Scope and Organization ofthe Book................... 1.6 Bibliographic Notes........................... 1.7 Exercises ......................................................................................... 21 24 25 27 29 33 35 41 2 Data 2.1 Types of Data................................................................................... 2.1.1 Attributes and Measurement........................................... 2.1.2 Types of Data Sets . ......................... 2.2 Data Quality......................................................................... 2.2.1 Measurement and Data Collection Issues ......................... 2.2.2 Issues Related to Applications ........................................ 2.3 Data Preprocessing.................................................... 2.3.1 Aggregation................................................................ 2.3.2 Sampling....................................................... 2.3.3 Dimensionality Reduction.......................................... 2.3.4 Feature Subset Selection ............................ 2.3.5 Feature Creation................................. 2.3.6 Discretization and Binarization . . . ............................... 2.3.7
Variable Transformation................................. 2.4 Measures of Similarity andDissimilarity................... 2.4.1 Basics ................................................................. 2.4.2 Similarity and Dissimilarity between Simple Attributes . 2.4.3 Dissimilarities between Data Objects..................... 2.4.4 Similarities between Data Objects.................. 43 46 47 54 62 62 69 70 71 72 76 78 81 83 89 91 92 94 96 98
12 Contents 2.5 2.6 3 2.4.5 Examples of Proximity Measures..................................... 99 2.4.6 Mutual Information............................................................. 108 2.4.7 Kernel Functions*................................................................ 110 2.4.8 Bregman Divergence*.......................................................... 114 2.4.9 Issues in Proximity Calculation...........................................116 2.4.10 Selecting the Right Proximity Measure...............................118 Bibliographic Notes.............................................................................120 Exercises ............................................................................................ 125 Classification: Basic Concepts and Techniques 133 3.1 Basic Concepts...................................................................................134 3.2 General Framework for Classification.............................................. 137 3.3 Decision Tree Classifier ....................................................................139 3.3.1 A Basic Algorithm to Build a DecisionTree.......................141 3.3.2 Methods for Expressing Attribute Test Conditions . . . 144 3.3.3 Measures for Selecting an Attribute Test Condition . . . 147 3.3.4 Algorithm for Decision Tree Induction...............................156 3.3.5 Example Application: Web Robot Detection......................158 3.3.6 Characteristics of Decision Tree Classifiers.........................160 3.4 Model
Overfitting.................................................................................167 3.4.1 Reasons for Model Overfitting ...........................................169 3.5 Model Selection....................................................................................176 3.5.1 Using a Validation Set.......................................................... 176 3.5.2 Incorporating Model Complexity........................................ 177 3.5.3 Estimating Statistical Bounds.............................................. 182 3.5.4 Model Selection for Decision Trees.....................................182 3.6 Model Evaluation.................................................................................184 3.6.1 Holdout Method....................................................................185 3.6.2 Cross-Validation....................................................................185 3.7 Presence of Hyper-parameters.......................................................... 188 3.7.1 Hyper-parameter Selection ............ ... . .·՛......................188 3.7.2 Nested Cross-Validation....................................................... 190 3.8 Pitfalls of Model Selection and Evaluation.....................................192 3.8.1 Overlap between Training and Test Sets............................192 3.8.2 Use of Validation Error as Generalization Error............ 192 3.9 Model Comparison* ..........................................................................193 3.9.1 Estimating the Confidence Interval for Accuracy .... 194 3.9.2 Comparing the
Performance of Two Models..................... 195 3.10 Bibliographic Notes.............................................................................196 3.11 Exercises ............................................................................................ 205
Contents 13 4 Association Analysis: Basic Concepts and Algorithms 213 4.1 Preliminaries........................................... 214 4.2 Frequent Itemset Generation ................................. 218 4.2.1 The Apriori Principle ........................................... 219 4.2.2 Frequent Itemset Generation in the Apriori Algorithm . 220 4.2.3 Candidate Generation and Pruning .....................................224 4.2.4 Support Counting ................................................................ 229 4.2.5 Computational Complexity.................................... 233 4.3 Rule Generation............... 236 4.3.1 Confidence-Based Pruning.................................... 236 4.3.2 Rule Generation in Apriori Algorithm.................. 237 4.3.3 An Example: Congressional Voting Records..................... 238 4.4 Compact Representation of Frequent Itemsets.............................. 240 4.4.1 Maximal Frequent Itemsets................................. 240 4.4.2 Closed Itemsets...................................................................... 242 4.5 Alternative Methods for Generating Frequent Itemsets* .... 245 4.6 FP-Growth Algorithm* ....................................... 249 4.6.1 FP-Tree Representation.......................................... 250 4.6.2 Frequent Itemset Generation in FP-Growth Algorithm . 253 4.7 Evaluation of Association Patterns.................................................257 4.7.1 Objective Measures of Interestingness 258 4.7.2 Measures beyond Pairs of Binary Variables..................... 270 4.7.3 Simpson’s
Paradox............ ........................ ... . ................272 4.8 Effect of Skewed Support Distribution.................. 274 4.9 Bibliographic Notes............................................................................ 280 4.10 Exercises ............................................................................................294 5 Cluster Analysis: Basic Concepts and Algorithms 307 5.1 Overview ......................................................................... 310 5.1.1 What Is Cluster Analysis?........................................... 310 5.1.2 Different Types of Clusterings.............................. 311 5.1.3 Different Types of Clusters........................... 313 5.2 K-means........................... 316 5.2.1 The Basic К-means Algorithm............... 317 5.2.2 К-means: Additional Issues.................................... 326 5.2.3 Bisecting K-means....................................... 329 5.2.4 K-means and Different Types of Clusters . . .... . . 330 5.2.5 Strengths and Weaknesses............... 331 5.2.6 K-means as an Optimization Problem.............................. 331
14 Contents . 5.3 5.4 5.5 5.6 5.7 Agglomerative Hierarchical Clustering.......................................... 336 5.3.1 Basic Agglomerative Hierarchical Clustering Algorithm 337 5.3.2 Specific Techniques................................................................ 339 5.3.3 The Lance-Williams Formula for Cluster Proximity . . . 344 5.3.4 Key Issues in Hierarchical Clustering..................................345 5.3.5 Outliers 346 5.3.6 Strengths and Weaknesses.................................................... 347 DBSCAN............................................................................................ 347 5.4.1 Traditional Density: Center-Based Approach.................. 347 5.4.2 The DBSCAN Algorithm.................................................... 349 5.4.3 Strengths and Weaknesses........................... 351 Cluster Evaluation . .......................................................................... 353 5.5.1 Overview . ............................................................................. 353 5.5.2 Unsupervised Cluster Evaluation Using Cohesion and Separation .............................................................................356 5.5.3 Unsupervised Cluster Evaluation Using the Proximity Matrix......................................................................................364 5.5.4 Unsupervised Evaluation of Hierarchical Clustering . . . 367 5.5.5 Determining the CorrectNumber of Clusters....................369 5.5.6 Clustering Tendency............................................................. 370 5.5.7 Supervised
Measures of Cluster Validity........................... 371 5.5.8 Assessing the Significance of Cluster ValidityMeasures . 376 5.5.9 Choosing a Cluster Validity Measure................................. 378 Bibliographic Notes..........................................................................379 Exercises ............................................................................................385 6 Classification: Alternative Techniques 6.1 6.2 6.3 6.4 395 Types of Classifiers . .......................................................................... 395 Rule-Based Classifier..........................................................................397 6.2.1 How a Rule-Based Classifier Works.....................................399 6.2.2 Properties of a Rule Set.......................................................400 6.2.3 Direct Methods for Rule Extraction................................. 401 6.2.4 Indirect Methods for Rule Extraction .............................. 406 6.2.5 Characteristics of Rule-Based Classifiers........................... 408 Nearest Neighbor Classifiers............................... 410 6.3.1 Algorithm................................................................................411 6.3.2 Characteristics of Nearest Neighbor Classifiers ............... 412 Naive Bayes Classifier...................................................................... 414 6.4.1 Basics of Probability Theory.................. 415 6.4.2 Naïve Bayes Assumption....................................................... 420
Contents 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 15 Bayesian Networks.................................................... 429 6.5.1 Graphical Representation.......................................... 429 6.5.2 Inference and Learning.......................................... 435 6.5.3 Characteristics of Bayesian Networks........................ 444 Logistic Regression..................... 445 6.6.1 Logistic Regression as a Generalized Linear Model . . . 446 6.6.2 Learning Model Parameters.................. 447 6.6.3 Characteristics of Logistic Regression ...............................450 Artificial Neural Network (ANN)....................................................451 6.7.1 Perceptron ............................................................................ 452 6.7.2 Multi-layer Neural Network.................................................456 6.7.3 Characteristics of ANN .......................................................463 Deep Learning .................................................................................. 464 6.8.1 Using Synergistic Loss Functions 465 6.8.2 Using Responsive Activation Functions.............................. 468 6.8.3 Regularization...................................................................... 470 6.8.4 Initialization of Model Parameters . . ...............................473 6.8.5 Characteristics of Deep Learning....................................... 477 Support Vector Machine (SVM).......................................................478 6.9.1 Margin of a Separating Hyperplane....................................
478 6.9.2 Linear SVM.............................. 480 6.9.3 Soft-margin SVM . ........................................ 486 6.9.4 Nonlinear SVM........................................... 492 6.9.5 Characteristics of SVM .............................. 496 Ensemble Methods..........................................................................498 6.10.1 Rationale for Ensemble Method............... .................. 499 6.10.2 Methods for Constructing an Ensemble Classifier .... 499 6.10.3 Bias-Variance Decomposition.............................. 502 6.10.4 Bagging.................................................................................. 504 6.10.5 Boosting.................................... 507 6.10.6 Random Forests ................................................. 512 6.10.7 Empirical Comparison among Ensemble Methods .... 514 Class Imbalance Problem..................... 515 6.11.1 Building Classifiers with Class Imbalance........................ 516 6.11.2 Evaluating Performance with Class Imbalance ...... 520 6.11.3 Finding an Optimal Score Threshold ............................... 524 6.11.4 Aggregate Evaluation of Performance .............................. 525 Multiclass Problem .................. 532 Bibliographic Notes . .............................................. 535 Exercises .............................................. 547
16 Contents 7 Association Analysis: Advanced Concepts 559 7.1 Handling Categorical Attributes ...................... ...................... 559 7.2 Handling Continuous Attributes ........................ 562 7.2.1 Discretization-Based Methods..............................................562 7.2.2 Statistics-Based Methods....................................................566 7.2.3 Non-discretization Methods.............................................. 568 7.3 Handling a Concept Hierarchy ..................... 570 7.4 Sequential Patterns........................... 572 7.4.1 Preliminaries..................... 573 7.4.2 Sequential Pattern Discovery..............................................576 7.4.3 Timing Constraints*............................................................. 581 7.4.4 Alternative Counting Schemes*...........................................585 7.5 Subgraph Patterns.............................................................................587 7.5.1 Preliminaries . ...................... 588 7.5.2 Frequent Subgraph Mining .................................................591 7.5.3 Candidate Generation.................................... 595 7.5.4 Candidate Pruning................................................................ 601 7.5.5 Support Counting..................... 601 7.6 Infrequent Patterns* . . . .................................. 601 7.6.1 Negative Patterns ................................................................ 602 7.6.2 Negatively Correlated Patterns...........................................603 7.6.3 Comparisons among Infrequent
Patterns, Negative Patterns, and Negatively Correlated Patterns.................. 604 7.6.4 Techniques for Mining InterestingInfrequent Patterns . 606 7.6.5 Techniques Based on Mining NegativePatterns................ 607 7.6.6 Techniques Based on Support Expectation........................ 609 7.7 Bibliographic Notes.............................................................................613 7.8 Exercises ............................................................................................618 8 Cluster Analysis: Additional Issues and Algorithms 633 8.1 Characteristics of Data, Clusters, and Clustering Algorithms . 634 8.1.1 Example: Comparing К-means and DBSCAN.................. 634 8.1.2 Data Characteristics............................................................. 635 8.1.3 Cluster Characteristics . . . ................................................. 637 8.1.4 General Characteristics of ClusteringAlgorithms . . , . 639 8.2 Prototype-Based Clustering............................................................. 641 8.2.1 Fuzzy Clustering................................................................... 641 8.2.2 Clustering Using Mixture Models........................................647 8.2.3 Self-Organizing Maps (SOM) .............................................. 657 8.3 Density-Based Clustering ................................................................ 664
Contents 17 8.3.1 8.3.2 8.3.3 Grid-Based Clustering..........................................................664 Subspace Clustering............................................................. 668 DENCLUE: A Kernel-Based Scheme forDensity-Based Clustering............................................................................... 672 8.4 Graph-Based Clustering................................................................... 676 8.4.1 Sparsification......................................................................... 677 8.4.2 Minimum Spanning Tree (MST) Clustering..................... 678 8.4.3 OPOSSUM: Optimal Partitioning of Sparse Similarities Using METIS......................................................................... 679 8.4.4 Chameleon: Hierarchical Clustering withDynamic Modeling............................................................................... 680 8.4.5 Spectral Clustering................................................................ 686 8.4.6 Shared Nearest Neighbor Similarity ................................. 693 8.4.7 The Jarvis-Patrick Clustering Algorithm........................... 696 8.4.8 SNN Density......................................................................... 698 8.4.9 SNN Density-Based Clustering...........................................699 8.5 Scalable Clustering Algorithms.......................................................701 8.5.1 Scalability: General Issues and Approaches..................... 701 8.5.2 BIRCH...................................................................................704 8.5.3
CURE......................................................................... 706 8.6 Which Clustering Algorithm?...........................................................710 8.7 Bibliographic Notes............................................................................ 713 8.8 Exercises ............................................................................................719 9 Anomaly Detection 723 9.1 Characteristics of Anomaly Detection Problems........................... 725 9.1.1 A Definition of an Anomaly.................................................725 9.1.2 Nature of Data...................................................................... 726 9.1.3 How Anomaly Detection is Used....................................... 727 9.2 Characteristics of Anomaly Detection Methods........................... 728 9.3 Statistical Approaches.......................................................... 730 9.3.1 Using Parametric Models .................................................... 730 9.3.2 Using Non-parametric Models..............................................734 9.3.3 Modeling Normal and Anomalous Classes........................ 735 9.3.4 Assessing Statistical Significance....................................... 737 9.3.5 Strengths and Weaknesses....................................................738 9.4 Proximity-based Approaches............................................................. 739 9.4.1 Distance-based Anomaly Score...........................................739 9.4.2 Density-based Anomaly Score..............................................740 9.4.3
Relative Density-based Anomaly Score.............................. 742 9.4.4 Strengths and Weaknesses....................................................743
18 Contents 9.5 Clustering-based Approaches..........................................................744 9.5.1 Finding Anomalous Clusters ........................ 744 9.5.2 Finding Anomalous Instances............................................. 745 9.5.3 Strengths and Weaknesses....................................................748 9.6 Reconstruction-based Approaches.............................. 748 9.6.1 Strengths and Weaknesses....................................................751 9.7 One-class Classification ................................................................... 752 9.7.1 Use of Kernels.............................................. 753 9.7.2 The Origin Trick...................................................................754 9.7.3 Strengths and Weaknesses....................................................758 9.8 Information Theoretic Approaches................................................ 758 9.8.1 Strengths and Weaknesses....................................................760 9.9 Evaluation of Anomaly Detection....................................................760 9.10 Bibliographic Notes............................................................................ 762 9.11 Exercises ............................................................................................769 10 Avoiding False Discoveries 775 10.1 Preliminaries: Statistical Testing.................................... 776 10.1.1 Significance Testing .............................................................776 10.1.2 Hypothesis
Testing................................................................ 781 10.1.3 Multiple Hypothesis Testing................................................ 787 10.1.4 Pitfalls in Statistical Testing ..................................... . . 796 10.2 Modeling Null and Alternative Distributions.............................. 798 10.2.1 Generating Synthetic Data Sets.......................................... 801 10.2.2 Randomizing Class Labels.......................................... 802 10.2.3 Resampling Instances ..........................................................802 10.2.4 Modeling the Distribution of the Test Statistic............ 803 10.3 Statistical Testing for Classification..............................................803 10.3.1 Evaluating Classification Performance..................... 803 10.3.2 Binary Classification as Multiple Hypothesis Testing . . 805 10.3.3 Multiple Hypothesis Testing in Model Selection...............806 10.4 Statistical Testing for Association Analysis ................................. 807 10.4.1 Using Statistical Models.......................................... 808 10.4.2 Using Randomization Methods.......................................... 814 10.5 Statistical Testing for Cluster Analysis.................. .....................815 10.5.1 Generating a Null Distribution for Internal Indices . . . 816 10.5.2 Generating a Null Distribution forExternal Indices ... 818 10.5.3 Enrichment............................................................................ 818 10.6 Statistical Testing for Anomaly
Detection.................................... 820 10.7 Bibliographic Notes............................................................................ 823 10.8 Exercises ........................................................................................... 828
Contents 19 Author Index 836 Subject Index 849 Copyright Permissions 859
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| indexdate | 2024-07-10T00:54:55Z |
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| isbn | 9780273769224 0133128903 |
| language | English |
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| publisher | Pearson |
| record_format | marc |
| spelling | Tan, Pang-Ning Verfasser (DE-588)1121324711 aut Introduction to data mining Pang-Ning Tan (Michigan State University), Michael Steinbach (University of Minnesota), Anuj Karpatne (University of Minnesota), Vipin Kumar (University of Minnesota) Second edition, global edition New York Pearson [2020] © 2020 859 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Bereits 2019 im Buchhandel erhältlich. - Impressum: "Authorized adaptation from the United States edition, entitled Introduction to Data Mining, 2nd Edition, ISBN 978-0-13-312890-1" Data Mining (DE-588)4428654-5 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Data Mining (DE-588)4428654-5 s DE-604 Steinbach, Michael Verfasser (DE-588)101724216X aut Karpatne, Anuj Verfasser aut Kumar, Vipin 1956- Verfasser (DE-588)1068935294 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=026808579&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Tan, Pang-Ning Steinbach, Michael Karpatne, Anuj Kumar, Vipin 1956- Introduction to data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4151278-9 |
| title | Introduction to data mining |
| title_auth | Introduction to data mining |
| title_exact_search | Introduction to data mining |
| title_full | Introduction to data mining Pang-Ning Tan (Michigan State University), Michael Steinbach (University of Minnesota), Anuj Karpatne (University of Minnesota), Vipin Kumar (University of Minnesota) |
| title_fullStr | Introduction to data mining Pang-Ning Tan (Michigan State University), Michael Steinbach (University of Minnesota), Anuj Karpatne (University of Minnesota), Vipin Kumar (University of Minnesota) |
| title_full_unstemmed | Introduction to data mining Pang-Ning Tan (Michigan State University), Michael Steinbach (University of Minnesota), Anuj Karpatne (University of Minnesota), Vipin Kumar (University of Minnesota) |
| title_short | Introduction to data mining |
| title_sort | introduction to data mining |
| topic | Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026808579&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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