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研究生： 梅智皓
Chih-Hau Mei
論文名稱： 應用萬用演算法為基礎之整合分群法於App顧客區隔之研究
Applying Meta-heuristic Algorithms-based Clustering Ensembles Methods for App Customer Segmentation
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: 歐陽超
Chao Ou-Yang
蔡介元
Chieh-Yuan Tsai
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： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 100
中文關鍵詞： 分群分析 、萬用演算法 、整合分群 、基因演算法 、粒子群演算法 、人工蜂群演算法 、應用程式 、顧客區隔
外文關鍵詞： Clustering Analysis, Meta-heuristic Algorithms, Clustering Ensembles, GA, PSO, ABC, App, Customer Segmentation
相關次數： 點閱：305  下載：11 
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    • 由於資訊科技日新月異，新的分群分析方法不斷地被提出，但是沒有任何一個分群演算法可以對所有的資料集進行正確的分群。因此，本研究主要是提出一個萬用演算法結合整合分群演算法的模型，在整合分群的部分使用資料子集合和不同的分群演算法來產生分群結果，再用一致關聯矩陣整合所有的分群結果，在過程中亦使用主成分分析法來減少程式的運算時間。最後，則利用萬用演算法為基礎之K-means分群演算法去決定最終分群結果。本研究提出三個使用此模型的分群演算法，包括了實數型基因演算法為基礎之K-means整合分群演算法、粒子群演算法為基礎之K-means整合分群演算法、人工蜂群演算法為基礎之K-means整合分群演算法。接著，將它們對Iris、Wine、Tae、Flame資料集的分群結果和其他分群演算法進行比較，分群演算法有K-means分群演算法、單一連結聚合分群演算法、完整連結聚合分群演算法、平均連結聚合分群演算法、實數型基因演算法為基礎之分群演算法、粒子群演算法為基礎之分群演算法、人工蜂群演算法為基礎之分群演算法、實數型基因演算法為基礎之K-means分群演算法、粒子群演算法為基礎之K-means分群演算法、人工蜂群演算法為基礎之K-means分群演算法。實驗結果顯示實數型基因演算法為基礎之K-means整合分群演算法和人工蜂群演算法為基礎之K-means整合分群演算法比其他分群演算法能獲得更好的結果，此外，人工蜂群演算法為基礎之K-means整合分群演算法比實數型基因演算法為基礎之K-means整合分群演算法更能穩定地獲得最佳解，所以更應用在體重控制App的紀錄數據進行App顧客區隔分析，並進而為App發展商提供了一些改進策略的建議。

    Recently, many clustering algorithms have been presented. However, there is no clustering method which can process all datasets correctly. This study proposes a model of meta-heuristic algorithms combining with clustering ensembles. The proposed clustering ensembles use subsets of data and different algorithms to generate clustering results. Then, co-association matrix is applied to integrate all the clustering results. In the process, principal component analysis is employed to reduce the computational time. The meta-heuristic algorithms based K-means clustering algorithms are applied to determine the clustering results. There are three clustering algorithms proposed including real-coded genetic algorithm based K-means clustering (GKC), particle swarm optimization based K-means clustering (PSOKC), and artificial bee colony based K-means clustering (ABCKC). Their results are compared with these of other clustering algorithms, such as K-means, single linkage agglomerative hierarchical clustering (SLHC), complete linkage agglomerative hierarchical clustering (CLHC), average linkage agglomerative hierarchical clustering (ALHC), real-coded genetic algorithms based clustering (GC), particle swarm optimization based clustering (PSOC), artificial bee colony based clustering (ABCC), real-coded genetic algorithm based K-means clustering (GKC), particle swarm optimization based K-means clustering (PSOKC), and artificial bee colony based K-means clustering (ABCKC) for iris, wine, tae, and flame datasets. The experimental results indicate that GKCE and ABCKCE are able to obtain better solutions than other methods. Furthermore, ABCKCE can stably obtain the optimal solution than GKCE, so ABCKCE is applied to do the App customer segmentation for log files of the weight control App. Finally, this study provides some improving suggestions for the application developer.

    摘要 I Abstract II 誌謝 III Contents IV List of Tables VII List of Figures IX Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 3 1.3 Research Scope and Constrain 3 1.4 Research Framework 3 Chapter 2 Literature Review 5 2.1 Clustering Methods 5 2.1.1 K-means Clustering 5 2.1.2 Hierarchical Clustering 7 2.2 Meta-heuristic Algorithm Based Clustering Methods 9 2.2.1 Real-Coded Genetic Algorithm Based Clustering 9 2.2.2 Particle Swarm Optimization Based Clustering 11 2.2.3 Artificial Bee Colony Based Clustering 13 2.3 Clustering Ensembles 15 2.3.1 Generation Mechanisms 16 2.3.2 Consensus Functions 17 Chapter 3 Methodology 19 3.1 Methodology Framework 19 3.2 The Proposed Clustering Ensembles 20 3.2.1 Subsets of Data with Different Algorithms 21 3.2.2 Co-association Matrix 22 3.3 Principal Component Analysis 24 3.4 Meta-heuristic Algorithm Based K-means Clustering Method 25 3.4.1 Real-Coded Genetic Algorithm Based K-means Clustering 26 3.4.2 Particle Swarm Optimization Based K-means Clustering 28 3.4.3 Artificial Bee Colony Based K-means Clustering 30 Chapter 4 Experimental Results 34 4.1 Datasets 34 4.2 Performance Measurement 35 4.3 Preprocessing of Datasets 35 4.4 Parameters Determination 36 4.4.1 Parameters for Iris Dataset 40 4.4.2 Parameters for Wine Dataset 43 4.4.3 Parameters for Tae Dataset 46 4.4.4 Parameters for Flame Dataset 49 4.5 Computational Results 52 4.6 Statistical Hypothesis 53 Chapter 5 Model Evaluation Results 58 5.1 Model Dataset 58 5.2 Performance Measurement 60 5.3 Dataset Preprocessing 60 5.4 Parameters Determination 61 5.5 Computational Results 67 5.6 Statistical Hypothesis 68 5.7 Analysis 69 Chapter 6 Conclusions and Future Research 72 6.1 Conclusions 72 6.2 Contributions 72 6.3 Future Research 73 References 74 Appendix 77

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