臺灣博碩士論文加值系統

This study proposed three meta-heuristic based fuzzy K-modes algorithms for clustering binary data set. There are three metaheuristic methods applied, namely Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm, and Artificial Bee Colony (ABC) algorithm. These three algorithms are combined with k-modes algorithm. Their aim is to give better initial modes for the k-modes. Herein, the similarity between two instances is calculated using jaccard coefficient. The Jaccard coefficient is applied since the data set contains many zero values. In order to cluster a real data set about automobile suppliers in Taiwan, the proposed algorithms are verified using benchmark data set. The experiments results show that PSO K-modes and GA K-modes is better than ABC K-modes. Moreover, from case study results, GA fuzzy K-modes gives the smallest SSE and also has faster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes.

ABSTRACT
ACKNOWLEDGEMENTS
CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
1.1 Research Background
1.2 Research Objectives
1.3 Research Scope and Constraints
1.4 Research Framework
CHAPTER 2 LITERATURE REVIEW
2.1 Data Types
2.2 Measures for Binary Data
2.3 Fuzzy Clustering
2.3.1 Fuzzy sets
2.3.2 Fuzzy clusters
2.3.3 Fuzzy c-means
2.3.4 Fuzzy K-modes
2.4 Meta-heuristic Algorithms
2.4.1 Particle Swarm Optimization (PSO) Algorithm
2.4.2 Genetic Algorithm (GA) algorithm
2.4.3 Artificial Bee Colony (ABC) Algorithm
CHAPTER 3 RESEARCH METHODOLOGY
3.1 Data collection
3.2 Data Preprocessing
3.3 Proposed algorithms
3.3.1 PSO Fuzzy K-modes
3.3.2 GA Fuzzy K-modes
3.3.3 ABC Fuzzy K-modes
CHAPTER 4 EXPERIMENTAL RESULT
4.1 Experimental Results
4.1.1 Data Sets
4.1.2 Parameter Setup
4.2 Computational Result
4.3 Statistical Result
CHAPTER 5 CASE STUDY
5.1 Supplier Clustering
5.1.1 Problem Description
5.2 Performance Measurement
5.3 Application and Results
5.3.1 Tuning Parameter
5.3.2 SSE Results of Proposed Methods
5.3.3 Analysis similarity within cluster
CHAPTER 6 CONCLUSION
6.1 Conclusion
6.2 Contributions
6.3 Future Research
APPENDIX I GENERAL FACTORIAL DESIGN OF DETERMINING TUNING PARAMETERS FOR SOLVING FUZZY K-MODES CLUSTERING
APPENDIX II DETERMINATION CLUSTER OF PROPOSED ALGORITHM
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