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Estimating the number of data clusters via the contrast statistic

Author(s): Oleg Gorshkov | Yuriy Vihovanets | Yuriy Lyakh | Vitaliy Gurianov

Journal: Advances in Molecular Imaging
ISSN 2161-6728

Volume: 05;
Issue: 02;
Start page: 95;
Date: 2012;
Original page

Keywords: SOM Neural Network | Clustering | Gap Statistic | Silhouette Statistic

A new method (the Contrast statistic) for estimating the number of clusters in a set of data is proposed. The technique uses the output of self-organising map clustering algorithm, comparing the change in dependency of “Contrast” value upon clusters number to that expected under a uniform distribution. A simulation study shows that the Contrast statistic can be used successfully either, when variables describing the object in a multi-dimensional space are independent (ideal objects) or dependent (real biological objects).

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