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Multi-way distributional clustering via pairwise interactions (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bekkerman, Ron Mccallum, Andrew |
| Description | 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. 1. In ICML |
| File Format | |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Objective Function Extensive Empirical Study Bottom-up Clustering Multiple Pairwise Mutual Information Co-occurrence Data Enron Email Collection Novel Unsupervised Learning Scheme Multi-way Distributional Clustering Cluster Variable Pairwise Interaction Four-way Application Top-down Clustering Real-world Datasets Several Type Local Optimization Correction Routine |
| Content Type | Text |
| Resource Type | Article |
