Conditional Information Bottleneck Clustering
David C. Gondek, Thomas Frank Hofmann · 2003
We present an extension of the well-known information bottleneck framework, called conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score. This general approach can be utilized in a data mining context to extract relevant information that is at the same time novel relative to known properties or structures of the data. We present possible applications of the conditional information bottleneck in information retrieval and text mining for recovering non-redundant clustering solutions, including experimental results on the WebKB data set which validate the approach.