Unsupervised document classification using sequential information maximization

Noam Slonim*, Nir Friedman, Naftali Tishby

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

218 Scopus citations

Abstract

We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is guaranteed to converge to a local maximum of the information, as required by the original IB principle. Moreover, the time and space complexity are significantly improved. We apply this algorithm to unsupervised document classification. In our evaluation, on small and medium size corpora, the sIB is found to be consistently superior to all the other clustering methods we examine, typically by a significant margin. Moreover, the sIB results are comparable to those obtained by a supervised Naive Bayes classifier. Finally, we propose a simple procedure for trading cluster's recall to gain higher precision, and show how this approach can extract clusters which match the existing topics of the corpus almost perfectly.

Original languageAmerican English
Pages (from-to)129-136
Number of pages8
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
DOIs
StatePublished - 2002
EventProceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Tampere, Finland
Duration: 11 Aug 200215 Aug 2002

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