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Non-Redundant Data Clustering (2004)
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BibTeX
@MISC{Gondek04non-redundantdata,
author = {David Gondek and Thomas Hofmann},
title = {Non-Redundant Data Clustering },
year = {2004}
}
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Abstract
Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We present experimental results for applications in text mining and computer vision.
Keyphrases
non-redundant data clustering class structure text mining non-numeric attribute popular approach different type discovery process reveal novel data clustering optimization scheme conditional mutual information score subject conditional information bottleneck computer vision present experimental result negative relevance information finding class information bottleneck framework unknown aspect
