Rough-fuzzy Pattern Recognition
Applications in Bioinformatics and Medical ImagingeBook - 2012
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.