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Unpublished Paper
Multi-Way Distributional Clustering via Pairwise Interactions
  • Ron Bekkerman
  • Ran El-Yaniv
  • Andrew McCallum, University of Massachusetts - Amherst
We present a novel unsupervised learning scheme that simultaneously clusters variables of several types (e.g., documents, words and authors) based on pairwise interactions between the types, as observed in co-occurrence data. In this scheme, multiple clustering systems are generated aiming at maximizing an objective function that measures multiple pairwise mutual information between cluster variables. To implement this idea, we propose an algorithm that interleaves top-down clustering of some variables and bottom-up clustering of the other variables, with a local optimization correction routine. Focusing on document clustering we present an extensive empirical study of two-way, three-way and four-way applications of our scheme using six real-world datasets including the 20 Newsgroups (20NG) and the Enron email collection. Our multi-way distributional clustering (MDC) algorithms consistently and significantly outperform previous state-of-the-art information theoretic clustering algorithms.
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Ron Bekkerman, Ran El-Yaniv and Andrew McCallum. "Multi-Way Distributional Clustering via Pairwise Interactions" (2005)
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