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Pattern Recognition Algorithms for Data Mining : Chapman & Hall/CRC Computer Science & Data Analysis - Sankar K. Pal

Pattern Recognition Algorithms for Data Mining

Chapman & Hall/CRC Computer Science & Data Analysis

Hardcover Published: 27th May 2004
ISBN: 9781584884576
Number Of Pages: 280

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Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.

Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Industry Reviews

"Pattern Recognition Algorithms in Data Mining is a book that commands admiration. Its authors, Professors S.K. Pal and P. Mitra are foremost authorities in pattern recognition, data mining, and related fields. Within its covers, the reader finds an exceptionally well-organized exposition of every concept and every method that is of relevance to the theme of the book. There is much that is original and much that cannot be found in the literature. The authors and the publisher deserve our thanks and congratulations for producing a definitive work that contributes so much and in so many important ways to the advancement of both the theory and practice of recognition technology, data mining, and related fields. The magnum opus of Professors Pal and Mitra is must-reading for anyone who is interested in the conception, design, and utilization of intelligent systems." - from the Foreword by Lotfi A. Zadeh, University of California, Berkeley, USA "The book presents an unbeatable combination of theory and practice and provides a comprehensive view of methods and tools in modern KDD. The authors deserve the highest appreciation for this excellent monograph." - from the Foreword by Zdzislaw Pawlak, Polish Academy of Sciences, Warsaw" This volume provides a very useful, thorough exposition of the many facets of this application from several perspectives. I congratulate the authors of this volume and I am pleased to recommend it as a valuable addition to the books in this field." - from the Forword by Laveen N. Kanal, University of Maryland, College Park, USA.

Forewordp. xiii
Prefacep. xxi
List of Tablesp. xxv
List of Figuresp. xxvii
Introductionp. 1
Introductionp. 1
Pattern Recognition in Briefp. 3
Data acquisitionp. 4
Feature selection/extractionp. 4
Classificationp. 5
Knowledge Discovery in Databases (KDD)p. 7
Data Miningp. 10
Data mining tasksp. 10
Data mining toolsp. 12
Applications of data miningp. 12
Different Perspectives of Data Miningp. 14
Database perspectivep. 14
Statistical perspectivep. 15
Pattern recognition perspectivep. 15
Research issues and challengesp. 16
Scaling Pattern Recognition Algorithms to Large Data Setsp. 17
Data reductionp. 17
Dimensionality reductionp. 18
Active learningp. 19
Data partitioningp. 19
Granular computingp. 20
Efficient search algorithmsp. 20
Significance of Soft Computing in KDDp. 21
Scope of the Bookp. 22
Multiscale Data Condensationp. 29
Introductionp. 29
Data Condensation Algorithmsp. 32
Condensed nearest neighbor rulep. 32
Learning vector quantizationp. 33
Astrahan's density-based methodp. 34
Multiscale Representation of Datap. 34
Nearest Neighbor Density Estimatep. 37
Multiscale Data Condensation Algorithmp. 38
Experimental Results and Comparisonsp. 40
Density estimationp. 41
Test of statistical significancep. 41
Classification: Forest cover datap. 47
Clustering: Satellite image datap. 48
Rule generation: Census datap. 49
Study on scalabilityp. 52
Choice of scale parameterp. 52
Summaryp. 52
Unsupervised Feature Selectionp. 59
Introductionp. 59
Feature Extractionp. 60
Feature Selectionp. 62
Filter approachp. 63
Wrapper approachp. 64
Feature Selection Using Feature Similarity (FSFS)p. 64
Feature similarity measuresp. 65
Feature selection through clusteringp. 68
Feature Evaluation Indicesp. 71
Supervised indicesp. 71
Unsupervised indicesp. 72
Representation entropyp. 73
Experimental Results and Comparisonsp. 74
Comparison: Classification and clustering performancep. 74
Redundancy reduction: Quantitative studyp. 79
Effect of cluster sizep. 80
Summaryp. 82
Active Learning Using Support Vector Machinep. 83
Introductionp. 83
Support Vector Machinep. 86
Incremental Support Vector Learning with Multiple Pointsp. 88
Statistical Query Model of Learningp. 89
Query strategyp. 90
Confidence factor of support vector setp. 90
Learning Support Vectors with Statistical Queriesp. 91
Experimental Results and Comparisonp. 94
Classification accuracy and training timep. 94
Effectiveness of the confidence factorp. 97
Margin distributionp. 97
Summaryp. 101
Rough-fuzzy Case Generationp. 103
Introductionp. 103
Soft Granular Computingp. 105
Rough Setsp. 106
Information systemsp. 107
Indiscernibility and set approximationp. 107
Reductsp. 108
Dependency rule generationp. 110
Linguistic Representation of Patterns and Fuzzy Granulationp. 111
Rough-fuzzy Case Generation Methodologyp. 114
Thresholding and rule generationp. 115
Mapping dependency rules to casesp. 117
Case retrievalp. 118
Experimental Results and Comparisonp. 120
Summaryp. 121
Rough-fuzzy Clusteringp. 123
Introductionp. 123
Clustering Methodologiesp. 124
Algorithms for Clustering Large Data Setsp. 126
Clarans: Clustering large applications based upon randomized searchp. 126
Birch: Balanced iterative reducing and clustering using hierarchiesp. 126
Dbscan: Density-based spatial clustering of applications with noisep. 127
Sting: Statistical information gridp. 128
CemmiStri: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initializationp. 129
Mixture model estimation via EM algorithmp. 130
Rough set initialization of mixture parametersp. 131
Mapping reducts to mixture parametersp. 132
Graph-theoretic clustering of Gaussian componentsp. 133
Experimental Results and Comparisonp. 135
Multispectral Image Segmentationp. 139
Discretization of image bandsp. 141
Integration of EM, MST and rough setsp. 141
Index for segmentation qualityp. 141
Experimental results and comparisonp. 141
Summaryp. 147
Rough Self-Organizing Mapp. 149
Introductionp. 149
Self-Organizing Maps (SOM)p. 150
Learningp. 151
Effect of neighborhoodp. 152
Incorporation of Rough Sets in SOM (RSOM)p. 152
Unsupervised rough set rule generationp. 153
Mapping rough set rules to network weightsp. 153
Rule Generation and Evaluationp. 154
Extraction methodologyp. 154
Evaluation indicesp. 155
Experimental Results and Comparisonp. 156
Clustering and quantization errorp. 157
Performance of rulesp. 162
Summaryp. 163
Classification, Rule Generation and Evaluation using Modular Rough-fuzzy MLPp. 165
Introductionp. 165
Ensemble Classifiersp. 167
Association Rulesp. 170
Rule generation algorithmsp. 170
Rule interestingnessp. 173
Classification Rulesp. 173
Rough-fuzzy MLPp. 175
Fuzzy MLPp. 175
Rough set knowledge encodingp. 176
Modular Evolution of Rough-fuzzy MLPp. 178
Algorithmp. 178
Evolutionary designp. 182
Rule Extraction and Quantitative Evaluationp. 184
Rule extraction methodologyp. 184
Quantitative measuresp. 188
Experimental Results and Comparisonp. 189
Classificationp. 190
Rule extractionp. 192
Summaryp. 199
Role of Soft-Computing Tools in KDDp. 201
Fuzzy Setsp. 201
Clusteringp. 202
Association rulesp. 203
Functional dependenciesp. 204
Data summarizationp. 204
Web applicationp. 205
Image retrievalp. 205
Neural Networksp. 206
Rule extractionp. 206
Clustering and self organizationp. 206
Regressionp. 207
Neuro-fuzzy Computingp. 207
Genetic Algorithmsp. 208
Rough Setsp. 209
Other Hybridizationsp. 210
Data Sets Used in Experimentsp. 211
Referencesp. 215
Indexp. 237
About the Authorsp. 243
Table of Contents provided by Rittenhouse. All Rights Reserved.

ISBN: 9781584884576
ISBN-10: 1584884576
Series: Chapman & Hall/CRC Computer Science & Data Analysis
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 280
Published: 27th May 2004
Publisher: CHAPMAN & HALL
Country of Publication: US
Dimensions (cm): 24.03 x 16.41  x 2.06
Weight (kg): 0.53
Edition Number: 1