Rough–Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Learn how to apply rough–fuzzy computing techniques to solveproblems in bioinformatics and medical image processing

Emphasizing applications in bioinformatics and medical imageprocessing, this text offers a clear framework that enables readersto take advantage of the latest rough–fuzzy computing techniques tobuild working pattern recognition models. The authors explain stepby step how to integrate rough sets with fuzzy sets in order tobest manage the uncertainties in mining large data sets. Chaptersare logically organized according to the major phases of patternrecognition systems development, making it easier to master suchtasks as classification, clustering, and feature selection.

Rough–Fuzzy Pattern Recognition examines the importantunderlying theory as well as algorithms and applications, helpingreaders see the connections between theory and practice. The firstchapter provides an introduction to pattern recognition and datamining, including the key challenges of working withhigh–dimensional, real–life data sets. Next, the authors exploresuch topics and issues as:

Soft computing in pattern recognition and data mining

A Mathematical framework for generalized rough sets,incorporating the concept of fuzziness in defining the granules aswell as the set

Selection of non–redundant and relevant features of real–valueddata sets

Selection of the minimum set of basis strings with maximuminformation for amino acid sequence analysis

Segmentation of brain MR images for visualization of humantissues

Numerous examples and case studies help readers betterunderstand how pattern recognition models are developed and used inpractice. This text covering the latest findings as well asdirections for future research is recommended for bothstudents and practitioners working in systems design, patternrecognition, image analysis, data mining, bioinformatics, softcomputing, and computational intelligence.

Foreword xiii

Preface xv

About the Authors xix

1 Introduction to Pattern Recognition and Data Mining1

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–MeansAlgorithm 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 Classification85

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 –InformationMeasures 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 SequenceAnalysis 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 Data201

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 Data225

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

Pradipta Maji, PhD, is Assistant Professor in the MachineIntelligence Unit of the Indian Statistical Institute. His researchexplores pattern recognition, bioinformatics, medical imageprocessing, cellular automata, and soft computing.

Sankar K. Pal, PhD, is Director and DistinguishedScientist of the Indian Statistical Institute. He is also a J. C.Bose Fellow of the Government of India. Dr. Pal founded both theMachine Intelligence Unit and the Center for Soft ComputingResearch at the Indian Statistical Institute. He is a Fellow of theIEEE, IAPR, IFSA, TWAS, and Indian National Science Academy.