Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2337
標題: 以距離競爭為基礎之分裂式階層分群演算法之探討
An Investigation on a Divisive Hierarchical Clustering Algorithm Based on Distance Competition
作者: 陳韋任
Chen, Wei-Jen
關鍵字: Cluster Algorithm
分群演算法
Euclidean Distance
Knee point
歐幾里得距離
膝點
出版社: 機械工程學系所
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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. 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摘要: 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。
URI: http://hdl.handle.net/11455/2337
其他識別: U0005-2108200914251300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2108200914251300
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