臺灣博碩士論文加值系統

由於資訊快速成長，複雜的資料能夠更容易的被蒐集。因此，如何從數據中找到重要的資訊，已變成非常重要的議題。在資料探勘中，分群分析是一個非常重要的技術。然而，沒有任何一種分群演算法能夠對所有不同的數據集進行正確的分群。因此，本研究提出多目標萬用演算法結合可能性直覺模糊c均值演算法(possibilistic intuitionistic fuzzy c-means, PIFCM)的新分群演算法。為了提供更好的分群結果，本研究考慮多目標分群而不是單目標分群。可能性直覺模糊c均值演算法結合了直覺模糊集合( intuitionistic fuzzy sets, IFSs)與可能性模糊c均值演算法(possibilistic fuzzy c-means, PFCM)。在本研究中，使用三種萬用演算法來改善分群結果，分別是實數型基因演算法、粒子群演算法和梯度進化演算法。因此，本研究提出三種分群演算法，分別是多目標實數型基因演算法為基礎之可能性直覺模糊c均值演算法(MOGA-PIFCM)、多目標粒子群演算法為基礎之可能性直覺模糊c均值演算法(MOPSO-PIFCM)及多目標梯度進化演算法為基礎之可能性直覺模糊c均值演算法(MOGE-PIFCM)。所提出的演算法將使用Ukm、Wine、Wbc、Tae、Vehicle、Pima、Iris、Breast、Liver及Banknote資料集的分群結果和其他分群演算法進行比較，包括直覺模糊c均值演算法、可能性直覺模糊c均值演算法、單目標實數型基因演算法為基礎之可能性直覺模糊c均值演算法、單目標粒子群演算法為基礎之可能性直覺模糊c均值演算法及單目標梯度進化演算法為基礎之可能性直覺模糊c均值演算法。本研究所使用的評比指標為Adjusted Rand Index (ARI)及準確率，根據實驗結果，MOGE PIFCM演算法比起其他演算法能夠獲得更佳的結果。

Due to the rapid growth of information, complicated data and information can be collected more easily. Therefore, how to reveal important information from the data becomes a very important issue. Clustering analysis is an important technique in data mining. However, there is no clustering method which can correctly cluster all different datasets. Thus, this study proposes hybrid of multi-objective meta-heuristics and possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm. In order to provide a better clustering result, this study considers multi-objective clustering instead of single-objective clustering. The PIFCM algorithm combines Atanassov’s intuitionistic fuzzy set (IFSs) with possibilistic fuzzy c-means, (PFCM). In this study, three meta heuristic algorithms are used to improve the clustering results, which are genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and gradient evolution (GE) algorithm. Therefore, there are three clustering algorithms which are proposed including multi-objective GA-based PIFCM (MOGA-PIFCM), multi-objective PSO based PIFCM (MOPSO-PIFCM), and multi-objective GE based PIFCM (MOGE-PIFCM). Their results are compared with those of other clustering algorithms, such as intuitionistic fuzzy c-means (IFCM), possibilistic intuitionistic fuzzy c-means (PIFCM), single-objective GA-PIFCM, single-objective PSO-PIFCM, and single-objective GE-PIFCM using Ukm, Wine, Wbc, Tae, Vehicle, Pima, Iris, Breast, Liver, and Banknote datasets. Adjusted Rand Index and accuracy measures are employed as performance indices for comparison. The experimental result shows that the MOGE-PIFCM can obtain better solutions than the other clustering algorithms in terms of all performance validation indices.

摘要 I
ABSTRACT II
誌謝 III
CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Research Objectives 2
1.3 Research Scope and Constraints 3
1.4 Research Framework 3
CHAPTER 2 LITERATURE REVIEW 5
2.1 Cluster Analysis 5
2.2 Fuzzy c-means Algorithm 7
2.3 Intuitionistic Fuzzy c-means Algorithm 9
2.3.1 Intuitionistic fuzzy theory 9
2.3.2 Intuitionistic fuzzy c-means algorithm 11
2.4 Possibilistic Fuzzy c-means Algorithm 12
2.5 Multi-objective Optimization Algorithm 15
2.5.1 Multi-objective genetic algorithm 16
2.5.2 Multi-objective particle swarm optimization algorithm 19
2.5.3 Multi-objective gradient evolution algorithm 21
CHAPTER 3 METHODOLOGY 24
3.1 Methodology Framework 24
3.2 Objective Functions 25
3.3 Data Preprocessing 26
3.4 Possibilistic Intuitionistic Fuzzy c-means Algorithm 26
3.5 Multi-objective Meta-heuristic Algorithm-based PIFCM Algorithm 28
3.5.1 Multi-objective genetic algorithm-based PIFCM algorithm 29
3.5.2 Multi-objective particle swarm optimization-based PIFCM algorithm 33
3.5.3 Multi-objective gradient evolution-based PIFCM algorithm 37
3.6 Select the Optimal Solution 40
CHAPTER 4 EXPERIMENTAL RESULTS 42
4.1 Datasets 42
4.2 Performance Measurement 43
4.3 Parameters Setting 44
4.4 Computational Results 47
4.5 Statistical Hypothesis 53
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH 57
5.1 Conclusions 57
5.2 Contributions 57
5.3 Future Research 57
REFERENCES 59
APPENDIX 65

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