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Activity Number: 112
Type: Contributed
Date/Time: Monday, July 30, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #309899
Title: On the Number of Clusters in a Dataset
Author(s): Ahmed Albatineh*+
Companies: Nova Southeastern University
Address: Division of MST, Fort Lauderdale, FL, 33314,
Keywords: Similarity index ; Clustering algorithm ; Circular data ; Bivariate normal mixture ; von Mises distribution ; Correction for chance agreement
Abstract:

This paper develops a method for determining the number of clusters in a data set by studying the behavior of the corrected similarity indices between two clustering methods. The proposed method is applied to a simulated bivariate normal data and further extended for use in circular data. Its performance is compared to some criteria in SAS software. Overall, the proposed method was found to perform very well. The effect of cluster size, number of clusters, dimensionality, and covariance structure have been investigated. The method is not based on any distributional or data assumption which makes it widely applicable. The results using real data example is given.

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Revised September, 2007