Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Authors:
Manjeet Rege;Ming Dong;Farshad Fotouhi
Affiliations:
Wayne State University, USA;Wayne State University, USA;Wayne State University, USA
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Abstract

In this paper, we present a novel graph theoretic approach to the problem of document-word co-clustering. In our approach, documents and words are modeled as the two vertices of a bipartite graph. We then propose Isoperimetric Co-clustering Algorithm (ICA) - a new method for partitioning the document-word bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over spectral approach in terms of the quality, efficiency and stability in partitioning the document-word bipartite graph.