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

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