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

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

by Sankar K. Pal, Pradipta Maji
Publisher: Wiley-IEEE Computer Society Press
Release Date: February 2012
ISBN: 9781118119716
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Book Description

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.

    Table of Contents

    1. Cover
    2. Series Page
    3. Title Page
    4. Copyright
    5. Dedication Page
    6. Foreword
    7. Preface
    8. About the Authors
    9. Chapter 1: Introduction to Pattern Recognition and Data Mining
      1. 1.1 Introduction
      2. 1.2 Pattern Recognition
      3. 1.3 Data Mining
      4. 1.4 Relevance of Soft Computing
      5. 1.5 Scope and Organization of the Book
      6. References
    10. Chapter 2: Rough-Fuzzy Hybridization and Granular Computing
      1. 2.1 Introduction
      2. 2.2 Fuzzy Sets
      3. 2.3 Rough Sets
      4. 2.4 Emergence of Rough-Fuzzy Computing
      5. 2.5 Generalized Rough Sets
      6. 2.6 Entropy Measures
      7. 2.7 Conclusion and Discussion
      8. References
    11. Chapter 3: Rough-Fuzzy Clustering: Generalized c-Means Algorithm
      1. 3.1 Introduction
      2. 3.2 Existing c-Means Algorithms
      3. 3.3 Rough-Fuzzy-Possibilistic c-Means
      4. 3.4 Generalization of Existing c-Means Algorithms
      5. 3.5 Quantitative Indices for Rough-Fuzzy Clustering
      6. 3.6 Performance Analysis
      7. 3.7 Conclusion and Discussion
      8. References
    12. Chapter 4: Rough-Fuzzy Granulation and Pattern Classification
      1. 4.1 Introduction
      2. 4.2 Pattern Classification Model
      3. 4.3 Quantitative Measures
      4. 4.4 Description of Data Sets
      5. 4.5 Experimental Results
      6. 4.6 Conclusion and Discussion
      7. References
    13. Chapter 5: Fuzzy-Rough Feature Selection using f-Information Measures
      1. 5.1 Introduction
      2. 5.2 Fuzzy-Rough Sets
      3. 5.3 Information Measure on Fuzzy Approximation Spaces
      4. 5.4 f-Information and Fuzzy Approximation Spaces
      5. 5.5 f-Information for Feature Selection
      6. 5.6 Quantitative Measures
      7. 5.7 Experimental Results
      8. 5.8 Conclusion and Discussion
      9. References
    14. Chapter 6: Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis
      1. 6.1 Introduction
      2. 6.2 Bio-Basis Function and String Selection Methods
      3. 6.3 Fuzzy-Possibilistic c-Medoids Algorithm
      4. 6.4 Rough-Fuzzy c-Medoids Algorithm
      5. 6.5 Relational Clustering for Bio-Basis String Selection
      6. 6.6 Quantitative Measures
      7. 6.7 Experimental Results
      8. 6.8 Conclusion and Discussion
      9. References
    15. Chapter 7: Clustering Functionally Similar Genes from Microarray Data
      1. 7.1 Introduction
      2. 7.2 Clustering Gene Expression Data
      3. 7.3 Quantitative and Qualitative Analysis
      4. 7.4 Description of Data Sets
      5. 7.5 Experimental Results
      6. 7.6 Conclusion and Discussion
      7. References
    16. Chapter 8: Selection of Discriminative Genes from Microarray Data
      1. 8.1 Introduction
      2. 8.2 Evaluation Criteria for Gene Selection
      3. 8.3 Approximation of Density Function
      4. 8.4 Gene Selection using Information Measures
      5. 8.5 Experimental Results
      6. 8.6 Conclusion and Discussion
      7. References
    17. Chapter 9: Segmentation of Brain Magnetic Resonance Images
      1. 9.1 Introduction
      2. 9.2 Pixel Classification of Brain MR Images
      3. 9.3 Segmentation of Brain MR Images
      4. 9.4 Experimental Results
      5. 9.5 Conclusion and Discussion
      6. References
    18. Index