JSM 2005 - Toronto

Abstract #304326

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Activity Number: 187
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304326
Title: A Criterion To Find the Optimal Size in Tight Clustering
Author(s): Lung-An Li*+ and Feng-Chang Lin and George C. Tseng
Companies: Institute of Statistical Science, Academia Sinica and University of Wisconsin, Madison and University of Pittsburgh
Address: NanKang, Taipei, 11529, Taiwan
Keywords: Resampling ; Scattered points ; Unsupervised learning
Abstract:

According to Tseng and Wong (2005) , some "scattered points" are not assigned into clusters. In their approach, there are two steps to find the tight clusters. At first, an algorithm A is used to choose candidate observations of tight clusters. In the second, tight clusters are obtained sequentially and the choice of the size is somehow user-specified. In this paper, a criterion to find the optimal size, and hence a new second step, is proposed to determine the size of tight clusters. A simulation study indicates this new proposed method seems very reasonably to agree with the true cluster sizes.

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