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

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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:. A Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set. Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis.

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

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.

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Sankar K. He is also a J. Bose Fellow of the Government of India. Request permission to reuse content from this site.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

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Articles Cited by Co-authors. International Journal of Approximate Reasoning 52 3 , , IEEE transactions on knowledge and data engineering 19 6 , ,