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研究生： Patipharn Amornnikun
Patipharn Amornnikun
論文名稱： 應用萬用演算法為基礎的可能性多變量模糊加權c-平均數演算法於市場區隔之研究
Metaheuristic-Based Possibilistic Multivariate Fuzzy Weighted C-Means Algorithms for Market Segmentation
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: 喻奉天
Vincent F. Yu
曹譽鐘
Yu-Chung Tsao
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： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 94
中文關鍵詞： 可能性多變量模糊加權c-平均數演算法 、混合型資料 、市場區隔 、萬用演算法 、正弦餘弦演算法
外文關鍵詞： Possibilistic multivariate fuzzy weighted c-means algorithm, Mixed data, Market segmentation, Meta-heuristics, Sine cosine algorithm
相關次數： 點閱：247  下載：1 
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    • 摘要 ………………………………………………………………………………………i ABSTRACT ………………………………………………………………………………ii ACKNOWLEDGEMENTS ………………………………………………………………iii TABLE OF CONTENTS ………………………………………………………………iv LIST OF FIGURES ………………………………………………………………………vii LIST OF TABLES ………………………………………………………………………viii CHAPTER 1 INTRODUCTION ………………………………………………………1 1.1 Research Background ………………………………………………………………1 1.2 Problem Definition ………………………………………………………………2 1.3 Research Objectives ………………………………………………………………2 1.4 Research Scope and Assumptions ………………………………………………3 1.5 Organization of Thesis ………………………………………………………………3 CHAPTER 2 LITERATURE SURVEY ………………………………………………5 2.1 Data Mining ………………………………………………………………………5 2.2 Data Features ………………………………………………………………………6 2.2.1 Numerical Data ………………………………………………………………6 2.2.2 Categorical Data ………………………………………………………………6 2.2.3 Mixed Data ………………………………………………………………7 2.3 Cluster Analysis ………………………………………………………………7 2.3.1 Overview of Clustering Approaches ………………………………………7 2.3.2 Fuzzy c-means Algorithm based Algorithm……………………………………. 8 2.3.2.1 Fuzzy c-means Algorithm …………………………………………………. 8 2.3.2.2 Multivariate Fuzzy c-means Algorithm ………………………………10 2.3.2.3 Possibilistic Multivariate Fuzzy c-means Algorithm ………………………11 2.3.3 Clustering Approaches for Mixed Attributes ………………………………12 2.3.3.1 Fuzzy k-prototype Algorithm ………………………………………………12 2.3.3.2 Fuzzy c-means Algorithms with Fuzzy p-mode Prototypes ………………13 2.3.3.3 Subspace Clustering ………………………………………………………14 2.4 Meta-heuristics ………………………………………………………………………15 2.4.1 Sine Cosine Algorithm (SCA) ………………………………………………16 2.4.2 Genetic Algorithm (GA) ………………………………………………………16 2.4.3 Particle Swarm Optimization (PSO) ………………………………………17 CHAPTER 3 METHODOLOGY ………………………………………………………18 3.1 Methodology Framework ………………………………………………………18 3.2 Possibilistic Multivariate Fuzzy Weighted c-means (PMFWCM) Algorithm ………19 3.3 Meta-heuristics Based Clustering ………………………………………………20 3.3.1 Sine Cosine Algorithm Based Clustering ………………………………………20 3.3.2 Genetic Algorithm Based Clustering ………………………………………23 3.3.3 Particle Swarm Optimization Algorithm Based Clustering ………………24 CHAPTER 4 COMPUTATIONAL RESULTS ………………………………………25 4.1 Data Sets ………………………………………………………………………25 4.2 Data Preprocessing ………………………………………………………………25 4.3 Parameter Setting ………………………………………………………………26 4.4 Experimental Result and Analysis ………………………………………………34 4.4.1 Computational Results ………………………………………………………34 4.4.2 Statistical Results ………………………………………………………………38 CHAPTER 5 CASE STUDY ………………………………………………………………42 5.1 Market Segmentation ………………………………………………………………42 5.1.1 Problem Description ………………………………………………………42 5.2 Number of Clusters ………………………………………………………………43 5.3 Results and Discussion ………………………………………………………………44 5.3.1 Tuning Parameter ………………………………………………………………44 5.3.2 SSE Results of Proposed Methods ………………………………………44 5.3.3 Clustering Results for Market Segmentation ………………………………47 CHAPTER 6 CONCLUSIONS AND FUTURE WORK ………………………………49 6.1 Conclusions ………………………………………………………………………49 6.2 Contributions ………………………………………………………………………49 6.3 Future Study ………………………………………………………………………49 REFERENCES ………………………………………………………………………50 APPENDIX I GENERAL FACTORIAL DESIGN OF DETERMINING TUNING PARAMETERS FOR SOLVING CLUSTERING ………………………………………52 APPENDIX II DETERMINATION CLUSTER OF PROPOSED ALGORITHM ………74 APPENDIX III QUESTIONNAIRE OF SOFT DRINK CONSUMERS ………………77

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