Pattern recognition algorithms for data mining : scalability, knowledge discovery and soft granular computing

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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.

Author Notes

Pabitra Mitra is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kanpur.

Foreword	p. xiii
Preface	p. xxi
List of Tables	p. xxv
List of Figures	p. xxvii
1 Introduction	p. 1
1.1 Introduction	p. 1
1.2 Pattern Recognition in Brief	p. 3
1.2.1 Data acquisition	p. 4
1.2.2 Feature selection/extraction	p. 4
1.2.3 Classification	p. 5
1.3 Knowledge Discovery in Databases (KDD)	p. 7
1.4 Data Mining	p. 10
1.4.1 Data mining tasks	p. 10
1.4.2 Data mining tools	p. 12
1.4.3 Applications of data mining	p. 12
1.5 Different Perspectives of Data Mining	p. 14
1.5.1 Database perspective	p. 14
1.5.2 Statistical perspective	p. 15
1.5.3 Pattern recognition perspective	p. 15
1.5.4 Research issues and challenges	p. 16
1.6 Scaling Pattern Recognition Algorithms to Large Data Sets	p. 17
1.6.1 Data reduction	p. 17
1.6.2 Dimensionality reduction	p. 18
1.6.3 Active learning	p. 19
1.6.4 Data partitioning	p. 19
1.6.5 Granular computing	p. 20
1.6.6 Efficient search algorithms	p. 20
1.7 Significance of Soft Computing in KDD	p. 21
1.8 Scope of the Book	p. 22
2 Multiscale Data Condensation	p. 29
2.1 Introduction	p. 29
2.2 Data Condensation Algorithms	p. 32
2.2.1 Condensed nearest neighbor rule	p. 32
2.2.2 Learning vector quantization	p. 33
2.2.3 Astrahan's density-based method	p. 34
2.3 Multiscale Representation of Data	p. 34
2.4 Nearest Neighbor Density Estimate	p. 37
2.5 Multiscale Data Condensation Algorithm	p. 38
2.6 Experimental Results and Comparisons	p. 40
2.6.1 Density estimation	p. 41
2.6.2 Test of statistical significance	p. 41
2.6.3 Classification: Forest cover data	p. 47
2.6.4 Clustering: Satellite image data	p. 48
2.6.5 Rule generation: Census data	p. 49
2.6.6 Study on scalability	p. 52
2.6.7 Choice of scale parameter	p. 52
2.7 Summary	p. 52
3 Unsupervised Feature Selection	p. 59
3.1 Introduction	p. 59
3.2 Feature Extraction	p. 60
3.3 Feature Selection	p. 62
3.3.1 Filter approach	p. 63
3.3.2 Wrapper approach	p. 64
3.4 Feature Selection Using Feature Similarity (FSFS)	p. 64
3.4.1 Feature similarity measures	p. 65
3.4.2 Feature selection through clustering	p. 68
3.5 Feature Evaluation Indices	p. 71
3.5.1 Supervised indices	p. 71
3.5.2 Unsupervised indices	p. 72
3.5.3 Representation entropy	p. 73
3.6 Experimental Results and Comparisons	p. 74
3.6.1 Comparison: Classification and clustering performance	p. 74
3.6.2 Redundancy reduction: Quantitative study	p. 79
3.6.3 Effect of cluster size	p. 80
3.7 Summary	p. 82
4 Active Learning Using Support Vector Machine	p. 83
4.1 Introduction	p. 83
4.2 Support Vector Machine	p. 86
4.3 Incremental Support Vector Learning with Multiple Points	p. 88
4.4 Statistical Query Model of Learning	p. 89
4.4.1 Query strategy	p. 90
4.4.2 Confidence factor of support vector set	p. 90
4.5 Learning Support Vectors with Statistical Queries	p. 91
4.6 Experimental Results and Comparison	p. 94
4.6.1 Classification accuracy and training time	p. 94
4.6.2 Effectiveness of the confidence factor	p. 97
4.6.3 Margin distribution	p. 97
4.7 Summary	p. 101
5 Rough-fuzzy Case Generation	p. 103
5.1 Introduction	p. 103
5.2 Soft Granular Computing	p. 105
5.3 Rough Sets	p. 106
5.3.1 Information systems	p. 107
5.3.2 Indiscernibility and set approximation	p. 107
5.3.3 Reducts	p. 108
5.3.4 Dependency rule generation	p. 110
5.4 Linguistic Representation of Patterns and Fuzzy Granulation	p. 111
5.5 Rough-fuzzy Case Generation Methodology	p. 114
5.5.1 Thresholding and rule generation	p. 115
5.5.2 Mapping dependency rules to cases	p. 117
5.5.3 Case retrieval	p. 118
5.6 Experimental Results and Comparison	p. 120
5.7 Summary	p. 121
6 Rough-fuzzy Clustering	p. 123
6.1 Introduction	p. 123
6.2 Clustering Methodologies	p. 124
6.3 Algorithms for Clustering Large Data Sets	p. 126
6.3.1 Clarans: Clustering large applications based upon randomized search	p. 126
6.3.2 Birch: Balanced iterative reducing and clustering using hierarchies	p. 126
6.3.3 Dbscan: Density-based spatial clustering of applications with noise	p. 127
6.3.4 Sting: Statistical information grid	p. 128
6.4 CemmiStri: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initialization	p. 129
6.4.1 Mixture model estimation via EM algorithm	p. 130
6.4.2 Rough set initialization of mixture parameters	p. 131
6.4.3 Mapping reducts to mixture parameters	p. 132
6.4.4 Graph-theoretic clustering of Gaussian components	p. 133
6.5 Experimental Results and Comparison	p. 135
6.6 Multispectral Image Segmentation	p. 139
6.6.1 Discretization of image bands	p. 141
6.6.2 Integration of EM, MST and rough sets	p. 141
6.6.3 Index for segmentation quality	p. 141
6.6.4 Experimental results and comparison	p. 141
6.7 Summary	p. 147
7 Rough Self-Organizing Map	p. 149
7.1 Introduction	p. 149
7.2 Self-Organizing Maps (SOM)	p. 150
7.2.1 Learning	p. 151
7.2.2 Effect of neighborhood	p. 152
7.3 Incorporation of Rough Sets in SOM (RSOM)	p. 152
7.3.1 Unsupervised rough set rule generation	p. 153
7.3.2 Mapping rough set rules to network weights	p. 153
7.4 Rule Generation and Evaluation	p. 154
7.4.1 Extraction methodology	p. 154
7.4.2 Evaluation indices	p. 155
7.5 Experimental Results and Comparison	p. 156
7.5.1 Clustering and quantization error	p. 157
7.5.2 Performance of rules	p. 162
7.6 Summary	p. 163
8 Classification, Rule Generation and Evaluation using Modular Rough-fuzzy MLP	p. 165
8.1 Introduction	p. 165
8.2 Ensemble Classifiers	p. 167
8.3 Association Rules	p. 170
8.3.1 Rule generation algorithms	p. 170
8.3.2 Rule interestingness	p. 173
8.4 Classification Rules	p. 173
8.5 Rough-fuzzy MLP	p. 175
8.5.1 Fuzzy MLP	p. 175
8.5.2 Rough set knowledge encoding	p. 176
8.6 Modular Evolution of Rough-fuzzy MLP	p. 178
8.6.1 Algorithm	p. 178
8.6.2 Evolutionary design	p. 182
8.7 Rule Extraction and Quantitative Evaluation	p. 184
8.7.1 Rule extraction methodology	p. 184
8.7.2 Quantitative measures	p. 188
8.8 Experimental Results and Comparison	p. 189
8.8.1 Classification	p. 190
8.8.2 Rule extraction	p. 192
8.9 Summary	p. 199
A Role of Soft-Computing Tools in KDD	p. 201
A.1 Fuzzy Sets	p. 201
A.1.1 Clustering	p. 202
A.1.2 Association rules	p. 203
A.1.3 Functional dependencies	p. 204
A.1.4 Data summarization	p. 204
A.1.5 Web application	p. 205
A.1.6 Image retrieval	p. 205
A.2 Neural Networks	p. 206
A.2.1 Rule extraction	p. 206
A.2.2 Clustering and self organization	p. 206
A.2.3 Regression	p. 207
A.3 Neuro-fuzzy Computing	p. 207
A.4 Genetic Algorithms	p. 208
A.5 Rough Sets	p. 209
A.6 Other Hybridizations	p. 210
B Data Sets Used in Experiments	p. 211
References	p. 215
Index	p. 237
About the Authors	p. 243

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