Non-redundant data clustering

David Gondek, Thomas Hofmann
2006 Knowledge and Information Systems  
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
more » ... 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.
doi:10.1007/s10115-006-0009-7 fatcat:xn56x5qvtrfklefvvq4pmetdt4