Model selection for cluster data analysis
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United States of America Patent
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Feb 15, 2011
Grant Date -
Jun 12, 2008
app pub date -
Oct 30, 2007
filing date -
May 18, 2001
priority date (Note) -
Expired
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Abstract
A model selection method is provided for choosing the number of clusters, or more generally the parameters of a clustering algorithm. The algorithm is based on comparing the similarity between pairs of clustering runs on sub-samples or other perturbations of the data. High pairwise similarities show that the clustering represents a stable pattern in the data. The method is applicable to any clustering algorithm, and can also detect lack of structure. We show results on artificial and real data using a hierarchical clustering algorithm.
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