About the Book
This book provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models. Based on the existing as well as new results, the book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm and applications. Special emphasis has been given to applications in bioinformatics and medical image processing. The book is useful for graduate students and researchers in computer science, electrical engineering, system science, medical science, and information technology. Researchers and practitioners in industry and R&D laboratories will also benefit.
Table of Contents:
Foreword xiii Preface xv About the Authors xix 1 Introduction to Pattern Recognition and Data Mining 1 1.1 Introduction, 1 1.2 Pattern Recognition, 3 1.3 Data Mining, 6 1.4 Relevance of Soft Computing, 9 1.5 Scope and Organization of the Book, 10 2 Rough-Fuzzy Hybridization and Granular Computing 21 2.1 Introduction, 21 2.2 Fuzzy Sets, 22 2.3 Rough Sets, 23 2.4 Emergence of Rough-Fuzzy Computing, 26 2.5 Generalized Rough Sets, 29 2.6 Entropy Measures, 30 2.7 Conclusion and Discussion, 36 3 Rough-Fuzzy Clustering: Generalized c -Means Algorithm 47 3.1 Introduction, 47 3.2 Existing c -Means Algorithms, 49 3.4 Generalization of Existing c -Means Algorithms, 61 3.5 Quantitative Indices for Rough-Fuzzy Clustering, 65 3.6 Performance Analysis, 68 3.7 Conclusion and Discussion, 80 4 Rough-Fuzzy Granulation and Pattern Classification 85 4.1 Introduction, 85 4.2 Pattern Classification Model, 87 4.3 Quantitative Measures, 95 4.4 Description of Data Sets, 97 4.5 Experimental Results, 100 4.6 Conclusion and Discussion, 112 5 Fuzzy-Rough Feature Selection using f -Information Measures 117 5.1 Introduction, 117 5.2 Fuzzy-Rough Sets, 120 5.3 Information Measure on Fuzzy Approximation Spaces, 121 5.4 f -Information and Fuzzy Approximation Spaces, 125 5.5 f -Information for Feature Selection, 129 5.6 Quantitative Measures, 133 5.7 Experimental Results, 135 5.8 Conclusion and Discussion, 156 6 Rough Fuzzy c -Medoids and Amino Acid Sequence Analysis 161 6.1 Introduction, 161 6.2 Bio-Basis Function and String Selection Methods, 164 6.3 Fuzzy-Possibilistic c -Medoids Algorithm, 168 6.4 Rough-Fuzzy c -Medoids Algorithm, 172 6.5 Relational Clustering for Bio-Basis String Selection, 176 6.6 Quantitative Measures, 178 6.7 Experimental Results, 181 6.8 Conclusion and Discussion, 196 7 Clustering Functionally Similar Genes from Microarray Data 201 7.1 Introduction, 201 7.2 Clustering Gene Expression Data, 203 7.3 Quantitative and Qualitative Analysis, 207 7.4 Description of Data Sets, 209 7.5 Experimental Results, 212 7.6 Conclusion and Discussion, 217 8 Selection of Discriminative Genes from Microarray Data 225 8.1 Introduction, 225 8.2 Evaluation Criteria for Gene Selection, 227 8.3 Approximation of Density Function, 230 8.4 Gene Selection using Information Measures, 234 8.5 Experimental Results, 235 8.6 Conclusion and Discussion, 250 9 Segmentation of Brain Magnetic Resonance Images 257 9.1 Introduction, 257 9.2 Pixel Classification of Brain MR Images, 259 9.3 Segmentation of Brain MR Images, 264 9.4 Experimental Results, 277 9.5 Conclusion and Discussion, 283 References, 283 Index 287
