萬用演算法為基礎的密度峰值之可能性模糊c-平均演算法於顧客區隔

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研究生：	Muhammad Naufal Alfareza Muhammad Naufal Alfareza
論文名稱：	萬用演算法為基礎的密度峰值之可能性模糊c-平均演算法於顧客區隔 Metaheuristic-based Density Peak Possibilistic Fuzzy c-Means Algorithms for Customer Segmentation
指導教授：	郭人介 Ren-Jieh Kuo
口試委員:	林希偉 Shi-Woei Lin 王孔政 Kung-Jeng Wang
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：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	140
中文關鍵詞：	顧客區隔、密度峰值、遺傳演算法、啟發式演算法、可能性
外文關鍵詞：	Customer segmentation, Density peak clustering, Genetic algorithm, Metaheuristic, Possibilistic fuzzy c-means algorithm
相關次數：	點閱：327 下載：9
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為了滿足多樣化的客戶需求，市場區隔是公司要注意的重要議題。聚類分析是劃分客戶最常用的方法之一。然而，聚類分析存在一些挑戰，因為沒有任何一個聚類技術能夠解決所有問題，並為所有類型的數據提供良好的結果。本研究旨在提出一種新穎以萬用演算法為基礎的密度峰值之可能性模糊C-平均演算法。藉由使用十一個標竿資料集來評估所提出演算法之表現。相較於其他聚類演算法，本研究所提之DP-GA-PFCM演算法能夠解決初始聚類中心的敏感度，找到參數和聚類中心的最佳組合，從而在準確性、ARI和NMI方面提供更好及更穩定的結果。此外，所提出的演算法也應用於個案研究，包含最近一次消費、消費頻率和消費金額（RFM）變量的零售業務資料集，並找出有六個具有各自特徵的集群，並建議在每個集群上，實施不同的策略，以維持或吸引客戶。

To meet diverse customer needs, market segmentation is an important issue for a company to pay attention to. One of the most common techniques to segment the customers is by using clustering analysis. But, there are some challenges in clustering analysis. Nonetheless, there is no single clustering technique capable of solving all the problems and providing good results for all types of datasets. This study intends to propose a novel clustering algorithm with density peak clustering and a possibilistic fuzzy c-means algorithm integrated with a genetic algorithm. Using eleven benchmark datasets to evaluate the performance of the proposed algorithm, the DP-GA-PFCM algorithm capable to solve the sensitivity of initial cluster centers, find the optimal combination of parameters and cluster centers, provide better and more stable results in terms of accuracy, ARI, and NMI compared to other clustering algorithms. Furthermore, the proposed algorithm is applied for the case study dataset, a retail business dataset containing recency, frequency, and monetary (RFM) variables. There were six clusters formed with respective characteristics. It is strongly suggested to implement the different strategies on each cluster to maintain or attract customers.

摘要    i
ABSTRACT    ii
ACKNOWLEDGMENT    iii
CONTENTS    iv
LIST OF FIGURES    vi
LIST OF TABLES    vii
Chapter 1 INTRODUCTION    1
1.1    Research Background    1
1.2    Research Objectives    2
1.3    Research Limitations    3
1.4    Thesis Organization    3
Chapter 2 LITERATURE SURVEY    5
2.1    Density Peak Fuzzy C-Means Algorithm    5
2.2    Possibilistic Fuzzy c-Means Algorithm    8
2.3    Genetic Algorithm    9
2.4    Relevant Previous Studies    12
Chapter 3 METHODOLOGY    15
3.1    Methodology Framework    16
3.2    Data Preprocessing    17
3.3    DP-GA-PFCM Algorithm    17
3.4    Performance Measurement    20
Chapter 4 EXPERIMENTAL RESULTS    22
4.1    Datasets    22
4.2    Parameters Setup    23
4.3    Experimental Results    25
4.3.1    First-Stage Experimental Result    25
4.3.2    Second-Stage Experimental Result    26
4.4    Statistical Test    30
4.4.1    First-Stage Statistical Test    33
4.4.2    Second-Stage Statistical Test    38
4.5    Computational Time    45
Chapter 5 CASE STUDY    48
5.1    Data Description    48
5.2    Clustering Result    48
5.3    Analysis and Discussion    50
Chapter 6 CONCLUSIONS AND FUTURE RESEARCH    53
6.1.    Conclusions    53
6.2.    Contributions    53
6.3.    Future Research    54
REFERENCES    55
APPENDIX A. Particle Swarm Optimization Algorithm    60
APPENDIX B. Sine Cosine Algorithm    62
APPENDIX C. Forensic-Based Investigation Algorithm    64
APPENDIX D. Experimental results    66
APPENDIX E. Normality Test Results    99
APPENDIX F. First-stage Kruskal-Wallis and Dunn-Bonferroni Post-hoc Test    105
APPENDIX G. Second-stage Kruskal-Wallis and Dunn-Bonferroni Post-hoc Test    116
APPENDIX H. Convergence History    127


                                

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