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Full Screen- TITLE
- CERTIFICATE
- DECLARATION
- DEDICATION
- ACKNOWLEDGEMENT
- ABSTRACT
- CONTENTS
- LIST OF FIGURES
- LIST OF ALGORITHMS
- LIST OF TABLES
- 1. INTRODUCTION
- 1.1 The Significance of Data Clustering
- 1.2 Classical Approach to Data Clustering
- 1.3 Soft Computing and Data Clustering
- 1.4 A new approach to clustering with soft computing
- 1.5 Layout of the thesis
- 2. LITERATURE SURVEY: DATA CLUSTERING ALGORITHMS AND SOFT COMPUTING
- 2.1 In a Nutshell
- 2.2 Soft Computing
- 2.3 Genetic Algotrithms
- 2.4 Fuzzy Logic
- 2.5 Data Clustering
- 2.6 Clustering Algorithms: Soft Computing Approach
- 2.7 Sequential Data Clustering
- 2.8 Cluster Validity Indices
- 2.9 Conclusions
- 3 THE FUZZY GUIDED GENETIC ALGORITHM FOR DATA CLUSTERING
- 3.1 In a Nutshell
- 3.2 The Problem
- 3.3 The Model Overview
- 3.4 The Alogorithm
- 3.5 Performance of the algorithm
- 3.6 Comparison with other clustering algorithms
- 3.7 Conclusions
- 4. A JOURNEY INTO BIOLOGY
- 4.1 In a Nutshell
- 4.2 Biology Primer
- 4.3 Computational Prediction of Operons
- 4.4 Conclusion
- 5. APPLICATION: COMPUTATIONAL PREDICTION OF GENE CLUSTERS
- 5.1 In a Nutshell
- 5.2 Linking the application to the algorithm
- 5.3 Algorithm Implementation
- 5.4 Prediction of gene cluster in E.coli
- 5.5 Prediction of gene clusters in B.subtills
- 5.6 Prediction of gene clusters in M.Tuberculosis genome
- 5.7 Algorithm Analysis
- 5.8 Conclusion
- 6. GENERATION OF FUZZY MEMBERSHIP FUNCTIONS
- 6.1 In a nutshell
- 6.2 The design of fuzzy membership functions and rules
- 6.3 The design of the fuzzy fitness finder
- 6.4 The Fuzzy Guided Genetic Algorithm for Generation of Membership Functions
- 6.5 Analysis of Results
- 6.6 Conclusions
- 7. CONCLUSIONS
- 7.1 Salient Features of the Algorithm
- 7.2 Applications
- 7.3 Major Contributions of the Thesis
- 7.4 Scope for future Enhancements
- Publications
- BIBLIOGRAPHY