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研究生:陳澤元
研究生(外文):Tse-Yuan Chen
論文名稱:叢集整合技術之研究
論文名稱(外文):The Study of Cluster Ensemble
指導教授:林志麟林志麟引用關係
指導教授(外文):Jun-Lin Lin
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:31
中文關鍵詞:Cluster EnsembleK-meansFuzzy C-meansEvidence Accumulation
外文關鍵詞:Cluster EnsembleK-meansFuzzy C-meansEvidence Accumulation
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過去,研究發現叢集整合技術(Cluster Ensemble)能有效提高叢集演算法的穩健性和穩定性。本文透過實驗比較個別叢集演算法與叢集整合演算法之叢集結果。本文使用K-means與 Fuzzy C-means兩種個別叢集演算法以及使用證據累積(Evidence Accumulation, EA)作為叢集整合的方法。經過不同資料集的實驗測試後,發現屬性個數較多之資料集宜採用叢集整合演算法,屬性個數較少之資料集則宜直接執行個別叢集演算法許多次後取最佳的結果。

Recent studies have shown that cluster ensemble improves the robustness and stability of individual clustering algorithms. This paper compares the clustering results of individual clustering algorithms and of cluster ensemble algorithm. K-means and Fuzzy C-means are used as individual clustering algorithms, and their results are combined using evidence accumulation for cluster ensemble algorithm. Our experimental results with several datasets show that, for datasets with many features, cluster ensemble algorithms are more suitable than individual clustering algorithms, but for datasets with few features, individual clustering algorithms are better.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 2
第二章 文獻探討 3
2.1 K-means 3
2.2 Fuzzy C-means 4
2.3 叢集整合技術 5
第三章 研究方法 9
3.1 叢集整合演算法 9
3.1.1 個別叢集演算法 9
3.1.2 Co-association matrix 10
3.1.3從Co-association matrix得到最終叢集結果 16
3.2 個別叢集演算法與叢集整合演算法之方法比較架構 18
第四章 實驗與討論 21
4.1 實驗資料集 21
4.2 實驗結果 21
第五章 結論與未來展望 28
參考文獻 29



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[2] Jain, A. K., Murty, M. N. and Flynn, P. J. (1999), Data clustering:A review, ACM Computing Surveys, 31(3), pp. 264-323.

[3] Fred, A. L. N. (2001), Finding Consistent Clusters in Data Partitions, Lecture Notes in Computer Science, 2096, pp. 309-318.

[4] Strehl, A. and Ghosh, J. (2003), Cluster Ensembles:A Knowledge Reuse Framework for Combining Multiple Partitions, Journal of Machine Learning Research, 3, pp. 583-617.

[5] MacQueen, J. B. (1967), Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1, pp. 281-297.

[6] Dunn, J. C. (1973), A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-separated Clusters, Journal of Cybernetics, 3 (3), pp. 32-57.

[7] Bezdek, J. C. (1981), Pattern Recognition with Fuzzy Objective Function Algoritms, Plenum Press, New York.
[8] Minaei-Bidgoli, B., Topchy, A. and Punch, W. F. (2004), A Comparison of Resampling Methods for Clustering Ensembles, Intl. Conf. on Machine Learning, Models; Technologies and Applications, pp. 939-945.

[9] Fred, A. L. N. and Jain, A. K.(2005), Combining multiple clusterings using evidence accumulation, IEEE Trans. Pattern Analysis Machine Intelligence, 27, pp. 835-850.

[10] Avogadri, R. and Valentini, G. (2008), Ensemble Clustering with a Fuzzy Approach, Studies in Computational Intelligence, 126, pp. 49-69.

[11] Fern, X. Z. and Brodley, C. E. (2003), Random projection for high dimensional clustering:A cluster ensemble approach, Proc. 20th Int''l Conf. Machine Learning.

[12] Wang, T. (2009), Comparing hard and Fuzzy C-means for evidence-accumulation clustering, Fuzzy Systems. FUZZ-IEEE, pp. 468-473.

[13] Fred, A. L. N. and Jain, A. K. (2002), Data clustering using evidence accumulation, in Proc. of the 16th Int’l Conference on Pattern Recognition, pp. 276-280.

[14] Fred, A. L. N. and Jain, A. K. (2002), Evidence accumulation clustering based on the K-means algorithm, in Structural, Syntactic, and Statistical Pattern Recognition, 2396, pp. 442-451.

[15] Asuncion, A. and Newman, D. J. (2010), UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

[16] Domeniconi, C., Papadopoulos, D., Gunopulos, D. and Ma, S. (2004), Subspace clustering of high dimensional data, SIAM International Conference on Data Mining.

[17] Al-Razgan, M. and Domeniconi, C. (2006), Weighted Clustering Ensembles, SIAM International Conference on Data Mining.

[18] Domeniconi, C. and Al-Razgan, M. (2009), Weighted Clustering Ensembles: Methods and analysis, ACM Transactions on Knowledge Discovery from Data, 2(4), pp. 1-40.


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