Non-redundant data clustering

8 years 12 months ago
Non-redundant data clustering
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.
David Gondek, Thomas Hofmann
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2007
Where KAIS
Authors David Gondek, Thomas Hofmann
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