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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4727


    Title: Two-phase clustering process for outliers detection
    Authors: M.F. Jiang;S.S. Tseng;C.M. Su
    Contributors: Department of Information Science and Applications
    Keywords: Outliers;k-means clustering;Two-phase clustering;MST
    Date: 2001-05
    Issue Date: 2009-11-30 16:03:18 (UTC+8)
    Publisher: Asia University
    Abstract: In this paper, a two-phase clustering algorithm for outliers detection is proposed. We first modify the traditional k-means algorithm in Phase 1 by using a heuristic ?if one new input pattern is far enough away from all clusters' centers, then assign it as a new cluster center?. It results that the data points in the same cluster may be most likely all outliers or all non-outliers. And then we construct a minimum spanning tree (MST) in Phase 2 and remove the longest edge. The small clusters, the tree with less number of nodes, are selected and regarded as outlier. The experimental results show that our process works well.
    Relation: Pattern Recognition Letters 22(6&7):691-700
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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