資料分群廣泛應用於各種領域，是一種藉由某特定的衡量指標將資料分類成群的方法。K-means (KM) 與 K-harmonic-means (KHM) 因簡單有效率是常見且基礎的分群工具。然而，KM與KHM皆有其本質上的缺陷，導致效能或效率不佳。本文，應用簡群演算法（simplified swarm optimization）分別針對KM與KHM的缺陷提出兩種分群演算法，藉此改善KM與KHM在分群上的效能與效率。
其次，由於資訊科技發展、網路崛起，催生大數據時代的來臨，許多現有的分群演算法無法在合理時間內處理大型資料集，其中包含KM與KHM。為改善提出之分群演算法在處理大型資料集之效率，本研究結合主成分分析與新式隨機取樣技術，發展一個新的分群架構。此一架構之原理是藉由同時減少資料集之維度與資料點之取用率，大幅提昇分群演算法的效率。
為驗證本研究提出之演算法與架構，所有實驗均使用真實資料集，實驗結果均與過去文獻比較。

Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. K-means (KM) and K-harmonic-means (KHM) are two common and fundamental clustering methods because of their simplicity and efficiency. However, both of them suffer from some problems. This study presents two novel algorithms based on simplified swarm optimization to deal with the drawbacks of KM and KHM, respectively.
In addition, with the advance of internet and information technologies, the data size is increasing explosively and many existing clustering approaches including KM and KHM are inefficiency for dealing with the large-size problem. For that, we propose a clustering framework by exploring the connection between principle component analysis and a novel random sampling technique into a procedure to increase the scalability of the proposed clustering algorithm.
To empirically evaluate the performance of the proposed methods, all experiments are examined using real-world datasets, and corresponding results are compared with recent works in the literature.

中文摘要 I
Abstract II
Acknowledgement III
Table of Contents IV
List of Tables VII
List of Figures X
Chapter 1. Introduction 1
1.1 Motivation 1
1.1.1 The drawbacks of K-means-type method 1
1.1.2 The drawbacks of K-harmonic-means-type method 2
1.1.3 Inefficiency on large-size problem 4
1.2 Objective and methodology 6
1.2.1 Objective for KM-type problem 6
1.2.2 Objective for KHM-type problem 7
1.2.3 Objective for clustering on large-size problem 7
1.3 Framework and organization 8
Chapter 2. Methodology 10
2.1 Clustering problem 10
2.2 K-means clustering 10
2.3 K-harmonic-means clustering 11
2.4 Taguchi method 12
2.5 Simplified swarm optimization 14
2.6 Principal component analysis 15
Chapter 3. Improved SSO for KM-type problem 18
3.1 Proposed methods 18
3.1.1 SSO clustering algorithm 18
3.1.2 Variable vibrating search (VVS) 19
3.1.3 Rapid centralized strategy (RCS) 21
3.1.4 The overall procedure of proposed method 23
3.2 Experiment results and discussion 25
3.2.1 Datasets 25
3.2.2 Parameter settings 26
3.2.3 Ex-1: Evaluation of RCS 27
3.2.4 Ex-2: Comparing ISSOKM with existing algorithms 30
3.2.5 Statistical analysis 35
3.3 Summary 37
Chapter 4. Improved SSO for KHM-type problem 40
4.1 Proposed methods 40
4.1.1 Initial population 40
4.1.2 Minimum movement strategy (MMS) 40
4.1.3 The overall procedure of proposed method 41
4.2 Experiment results and discussion 44
4.2.1 Datasets 44
4.2.2 Ex-1: Parameter settings 45
4.2.3 Ex-2: Comparing ISSOKHM with existing algorithms 49
4.2.4 Statistical analysis 55
4.3 Summary 58
Chapter 5. A framework for large-size problem 59
5.1 Empirical analysis of ISSOKM efficiency 59
5.2 Proposed framework 62
5.2.1 The first m PCs determination 62
5.2.2 Rolling random sampling (RRS) 62
5.2.3 The overall procedure of proposed framework 63
5.3 Experiment results and discussion 65
5.3.1 Datasets 66
5.3.2 Ex-1: parameter setting for Nsub and Nsam 67
5.3.3 Ex-2: comparing ISSOKMPR with existing algorithms 72
5.3.4 Time complexity of proposed algorithm 81
5.3.5 Statistical analysis 81
5.4 Summary 91
Chapter 6. Conclusion and future work 93
Reference 95

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