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研究生: Yuliana Portti
Yuliana - Portti
論文名稱: 應用萬用演算法為基礎之模糊K-modes演算法於供應商分群之研究
Application of Metaheuristic Based Fuzzy K-Modes Algorithm to Supplier Clustering
指導教授: 郭人介
Ren-Jieh Kuo
口試委員: 歐陽超
Chao Ou-Yang
周碩彥
Shuo-Yan Chou
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 83
外文關鍵詞: Fuzzy K-modes, Binary data set, Jaccard coefficient
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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 REFERENCES

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