Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Noam Slonim, Nir Friedman, Naftali Tishby
The information bottleneck method is an unsupervised model independent data organization technique. Given a joint distribution peA, B), this method con(cid:173) structs a new variable T that extracts partitions, or clusters, over the values of A that are informative about B. In a recent paper, we introduced a general princi(cid:173) pled framework for multivariate extensions of the information bottleneck method that allows us to consider multiple systems of data partitions that are inter-related. In this paper, we present a new family of simple agglomerative algorithms to construct such systems of inter-related clusters. We analyze the behavior of these algorithms and apply them to several real-life datasets.