JSM 2005 - Toronto

Abstract #304314

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 65
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: General Methodology
Abstract - #304314
Title: Determining the Number of Clusters in Data via the Weighted Gap Statistic
Author(s): Mingjin Yan*+
Companies: Virginia Polytechnic Institute and State University
Address: 1776 Liberty Ln Apt C34, Blacksburg, VA, 24060, United States
Keywords: cluster analysis ; the gap statistic ; the weighted gap statistic
Abstract:

Estimating the number of clusters in a dataset is a crucial step in cluster analysis. Although a large number of methods have been proposed to deal with this problem, a universally best performer has not been found by far. In this article, we propose new approaches of estimating the optimal number of clusters in data based on the weighted within-clusters sum of errors: a robust measurement of the within-clusters homogeneity. The methods are applicable when the input data contain only continuous measurements and a partitioning clustering method is used. The proposed methods are compared with other existing approaches using both simulated data and real data examples. It shows that the proposed methods are highly effective in determining the true number of clusters in a dataset.

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