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Informationen zum Autor PRADIPTA MAJI, PHD, is Assistant Professor in the Machine Intelligence Unit of the Indian Statistical Institute. His research explores pattern recognition, bioinformatics, medical image processing, cellular automata, and soft computing. SANKAR K. PAL, PHD, is Director and Distinguished Scientist of the Indian Statistical Institute. He is also a J. C. Bose Fellow of the Government of India. Dr. Pal founded both the Machine Intelligence Unit and the Center for Soft Computing Research at the Indian Statistical Institute. He is a Fellow of the IEEE, IAPR, IFSA, TWAS, and Indian National Science Academy. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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 1.2.1 Data Acquisition 4 1.2.2 Feature Selection 4 1.2.3 Classification and Clustering 5 1.3 Data Mining 6 1.3.1 Tasks, Tools, and Applications 7 1.3.2 Pattern Recognition Perspective 8 1.4 Relevance of Soft Computing 9 1.5 Scope and Organization of the Book 10 References 14 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 2.4.1 Granular Computing 26 2.4.2 Computational Theory of Perception and f -Granulation 26 2.4.3 Rough-Fuzzy Computing 28 2.5 Generalized Rough Sets 29 2.6 Entropy Measures 30 2.7 Conclusion and Discussion 36 References 37
