Rough-Fuzzy Pattern Recognition

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such 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 as well as the set Selection of non-redundant and relevant features of real-valued data sets Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis Segmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text covering the latest findings as well as directions for future research is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such 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 as well as the set* Selection of non-redundant and relevant features of real-valued data sets* Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis* Segmentation of brain MR images for visualization of human tissuesNumerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text--covering the latest findings as well as directions for future research--is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

Foreword xiiiPreface xvAbout the Authors xix1 Introduction to Pattern Recognition and Data Mining11.1 Introduction, 11.2 Pattern Recognition, 31.3 Data Mining, 61.4 Relevance of Soft Computing, 91.5 Scope and Organization of the Book, 102 Rough-Fuzzy Hybridization and Granular Computing 212.1 Introduction, 212.2 Fuzzy Sets, 222.3 Rough Sets, 232.4 Emergence of Rough-Fuzzy Computing, 262.5 Generalized Rough Sets, 292.6 Entropy Measures, 302.7 Conclusion and Discussion, 363 Rough-Fuzzy Clustering: Generalized c-MeansAlgorithm 473.1 Introduction, 473.2 Existing c-Means Algorithms, 493.4 Generalization of Existing c-Means Algorithms, 613.5 Quantitative Indices for Rough-Fuzzy Clustering, 653.6 Performance Analysis, 683.7 Conclusion and Discussion, 804 Rough-Fuzzy Granulation and Pattern Classification854.1 Introduction, 854.2 Pattern Classification Model, 874.3 Quantitative Measures, 954.4 Description of Data Sets, 974.5 Experimental Results, 1004.6 Conclusion and Discussion, 1125 Fuzzy-Rough Feature Selection using f -InformationMeasures 1175.1 Introduction, 1175.2 Fuzzy-Rough Sets, 1205.3 Information Measure on Fuzzy Approximation Spaces, 1215.4 f -Information and Fuzzy Approximation Spaces,1255.5 f -Information for Feature Selection, 1295.6 Quantitative Measures, 1335.7 Experimental Results, 1355.8 Conclusion and Discussion, 1566 Rough Fuzzy c-Medoids and Amino Acid SequenceAnalysis 1616.1 Introduction, 1616.2 Bio-Basis Function and String Selection Methods, 1646.3 Fuzzy-Possibilistic c-Medoids Algorithm, 1686.4 Rough-Fuzzy c-Medoids Algorithm, 1726.5 Relational Clustering for Bio-Basis String Selection,1766.6 Quantitative Measures, 1786.7 Experimental Results, 1816.8 Conclusion and Discussion, 1967 Clustering Functionally Similar Genes from Microarray Data2017.1 Introduction, 2017.2 Clustering Gene Expression Data, 2037.3 Quantitative and Qualitative Analysis, 2077.4 Description of Data Sets, 2097.5 Experimental Results, 2127.6 Conclusion and Discussion, 2178 Selection of Discriminative Genes from Microarray Data2258.1 Introduction, 2258.2 Evaluation Criteria for Gene Selection, 2278.3 Approximation of Density Function, 2308.4 Gene Selection using Information Measures, 2348.5 Experimental Results, 2358.6 Conclusion and Discussion, 2509 Segmentation of Brain Magnetic Resonance Images 2579.1 Introduction, 2579.2 Pixel Classification of Brain MR Images, 2599.3 Segmentation of Brain MR Images, 2649.4 Experimental Results, 2779.5 Conclusion and Discussion, 283References, 283Index 287