Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2337
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dc.contributor紀華偉zh_TW
dc.contributor屈岳陵zh_TW
dc.contributor盧銘詮zh_TW
dc.contributor.advisor蔡志成zh_TW
dc.contributor.author陳韋任zh_TW
dc.contributor.authorChen, Wei-Jenen_US
dc.contributor.other中興大學zh_TW
dc.date2010zh_TW
dc.date.accessioned2014-06-05T11:43:05Z-
dc.date.available2014-06-05T11:43:05Z-
dc.identifierU0005-2108200914251300zh_TW
dc.identifier.citation【Al-Daoud & Roberts, 1994】M. B. Al-Daoud and S. A. Roberts, “New Methods for the Initialization of Clusters,” Pattern Recognition Letters, Vol. 17, No. 5, pp. 451-455, May 1994. 【Bandyopadhyay & Maulik, 2002】S. Bandyopadhyay and U. Maulik, “An Evolutionary Technique Based on K-Means Algorithm for Optimal Clustering in RN,” Elsevier Information Sciences, Vol. 146, pp.221-237, October 2002. 【Bezdek & Pal, 1998】J. C. Bezdek and N. R. Pal, “Some New Indexes of Cluster Validity,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 28, No. 3, pp. 301-315, June 1998. 【Bhushan & Romagnoli, 2008】B. Bhushan and J. A. Romagnoli, “Self-Organizing Self-Clustering Network: A Strategy for Unsupervised Pattern Classification with Its Application to Fault Diagnosis,” Industrial and Engineering Chemistry Research, Vol. 47, No. 12, pp. 4209-4219, June, 2008. 【Chen, 2001】Z. Chen, “Data Mining and Uncertain Reasoning :An Integrated Approach,” Wiley, New York, 2001. 【Cheung, 2003】Y. M Cheung, “K*-Means: A New Generalized K-Means Clustering Algorithm,” Pattern Recognition Letters, Vol. 24, No. 15, pp. 2883-2893, November 2003. 【Chinrungrueng & Sequin, 1991】C. Chinrungrueng and C. H. Sequin, “Optimal Adaptive K-Means Algorithm with Dynamic Adjustment of Learning Rate,” Proceedings of IJCNN-91-Seattle: International Joint Conference on Neural Networks, pp. 855-862, 1991. 【Ester et al., 1996】M. Ester, H. P. Kriegel, J. Sander and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226-231, 1996. 【Everitt, 1993】B. S. Everitt, Cluster Analysis, Edward Arnold, New York, 1993. 【Frigui & Krishnapuram, 1999】H. Frigui and R. Krishnapuram, “A Robust Competitive Clustering Algorithm with Applications in Computer Vision,” IEEE Transactions of Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, pp.450-465, May 1999. 【Guha et al., 2001】S. Guha, R. Rastogi and K. Shim, “CURE: an Efficient Clustering Algorithm for Large Databases,” Information Systems, Vol. 26, No. 1, pp. 35-58, March 2001. 【Halkidi et al., 2001】M. Halkidi, Y. Batistakis and M. Vazirgiannis, “On Clustering Validation Techniques,” Journal of Intelligent Information Systems, Vol. 17, No. 2-3, pp. 107-145, December 2001. 【Huang, 1997】Z. Huang, “A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining,” SIGMOD, Data Mining Workshop, 1997. 【Joo & Lee, 2005】K. H. Joo and S. Lee, “An Incremental Document Clustering Algorithm Based on a Hierarchical Agglomerative Approach,” Lecture Notes in Computer Science, Vol. 3816 LNCS, pp. 321-332, 2005. 【Karypis et al., 1999】G. Karypis, E. H. Han and V. Kumar, “Chameleon: Hierarchical Clustering Using Dynamic Modeling,” Computer, Vol. 32, No. 8, pp. 68-75, August 1999. 【Kaufman & Rousseeuw, 1990】L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis, Wiley, New York, 1990. 【Ma & Zhang, 2004】D. Ma and A. Zhang, “An Adaptive Density-Based Clustering Algorithm for Spatial Database with Noise,” Proceedings - Fourth IEEE International Conference on Data Mining, ICDM, pp. 467-470, 2004. 【Ng & Han, 2002】R. T. Ng and J. Han, “CLARANS: A Method for Clustering Objects for Spatial Data Mining,” IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 5, pp. 1003-1016, September/October 2002. 【Pal & Biswas, 1997】N. R. Pal and J. Biswas, “Cluster Validation Using Graph Theoretic Concepts,” Pattern Recognition, Vol. 30, No. 6, pp. 847-857, June, 1997. 【Pilevar & Sukumar, 2005】A. H. Pilevar and M. Sukumar, “GCHL: A Grid-Clustering Algorithm for High-Dimensional Very Large Spatial Data Bases,” Pattern Recognition Letters, Vol. 26, No. 7, pp. 999-1010, May, 2005. 【Rhee et al., 2009】F. C. H. Rhee, K. S. Choi and B. I. Choi, “Kernel Approach to Possibilistic C-Means Clustering,” International Journal of Intelligent Systems, Vol. 24, No. 3, pp. 272-293, March 2009. 【Sheikholeslami et al., 1998】G. Sheikholeslami, S. Chatterjee and A. Zhang, “WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases,” Proceedings of the 24th VLDB Conference, New York, USA, pp. 428-439. 1998. 【Tsai et al., 2007】C. W. Tsai, C. S. Yang and M. C. Chiang, “A Time Efficient Pattern Reduction Algorithm for K-Means Based Clustering,” Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 504-509, 2007. 【Wang et al., 1997】W. Wang, J. Yang and R. Muntz, “STING: A Statistical Information Grid Approach to Spatial Data Mining,” Proceedings of the 23rd Very Large Databases Conference, pp. 186-195, 1997. 【Wu & Yang, 2002】K. L. Wu and M. S. Yang, “Alternative C-Means Clustering Algorithms,” Pattern Recognition, Vol. 35, No. 10, pp. 2267-2278, October 2002. 【Zhang et al., 1996】T. Zhang, R. Ramakrishnan and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” SIGMOD Record (ACM Special Interest Group on Management of Data), Vol. 25, No. 2, pp. 103-114, June 1996. 【林敬偉,2009】林敬偉,應用振動訊號與模糊演算法偵測刀具狀態之研究,國立中興大學機械工程研究所碩士論文,2009。 【陳品文,2007】陳品文,K-means群集演算法初始化之新方法,國立東華大學企業管理研究所碩士論文,2007。zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/2337-
dc.description.abstract在工程與科技領域裡,往往需要將大量的資料進行分群,而資料探勘領域中,群集分析技術可以協助使用者從大量的資料中挖掘資料間的結構、瞭解資料的複雜性,進而能夠擷取資料背後所隱含的資訊。本研究提出一個以距離競爭為基礎之分裂式階層分群演算法,它結合自組織映射圖網路的競爭概念,透過歐幾里得距離總和的計算,找出各群集之優勝資料點,並利用膝點決定收斂群集數,最後將資料點與各群集之優勝資料點重新競爭並進行分群,以找出各群集中心位置,作為後續分析的依據。 為了避開分群過程中優勝資料點選中奇異點的情況,本研究將第一優勝資料點選取方式由原先的競爭改以隨機選取,從第二優勝資料點才開始進行競爭,最後統計分群結果;此外,針對各群集所含資料點數差異過大所導致的分群數錯誤,本研究透過群心距離與群集半徑的計算,將分群錯誤之群集進行融合整併。經由上述兩種改良方法,可更進一步提高演算法分群結果的正確性與強健性。 本研究所發展之分群演算法經各式各樣的分群資料測試,顯示其強健性高。以二維的三群資料,各群資料點數相同且均為常態分佈之各種三角形為例,其演算法解析能力,即最短之兩群群心距離除以群集之直徑,介於1與2之間;而以其他文獻提供之資料測試,不論是群集差異、高維或是含有雜訊的資料,其計算分群結果亦與文獻之群集數相同;本研究進一步將他人的刀具磨耗實驗資料進行分群分析,亦能夠有效地瞭解該實驗蘊藏的內在意涵。zh_TW
dc.description.tableofcontents誌謝 III 摘要 IV Abstract V 目錄 VI 圖目錄 VIII 表目錄 X 符號表 XII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究方法與步驟 3 1.3 本文大綱 4 第二章 資料分群演算法 5 2.1 性能指標 5 2.2 相似度量測 6 2.3 分群演算法之種類 9 2.3.1 階層式分群演算法 9 2.3.2 分割式分群演算法 11 2.3.3 密度基礎式分群演算法 14 2.3.4 網格基礎式分群演算法 16 2.4 分群演算法之比較 18 第三章 以距離競爭為基礎之分裂式階層分群演算法 20 3.1 演算法概念 20 3.2 演算法建置 26 3.3 奇異點處理 28 3.4 群集再融合 32 3.5 計算複雜度分析 36 第四章 演算法測試與討論 41 4.1 虛擬資料測試 41 4.1.1 解析能力量化 41 4.1.2 群集差異測試 43 4.1.3 高維資料測試 46 4.1.4 雜訊測試 48 4.2 實驗資料測試 49 第五章 結論與未來展望 52 5.1結論 52 5.2未來展望 53 參考文獻 54 附錄 測試資料 57zh_TW
dc.language.isoen_USzh_TW
dc.publisher機械工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2108200914251300en_US
dc.subjectCluster Algorithmen_US
dc.subject分群演算法zh_TW
dc.subjectEuclidean Distanceen_US
dc.subjectKnee pointen_US
dc.subject歐幾里得距離zh_TW
dc.subject膝點zh_TW
dc.title以距離競爭為基礎之分裂式階層分群演算法之探討zh_TW
dc.titleAn Investigation on a Divisive Hierarchical Clustering Algorithm Based on Distance Competitionen_US
dc.typeThesis and Dissertationzh_TW
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