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Sixth IEEE International Conference on Data Mining (ICDM'06)
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
Hong Kong
December 18-December 22
ISBN: 0-7695-2701-9
Manjeet Rege, Wayne State University, USA
Ming Dong, Wayne State University, USA
Farshad Fotouhi, Wayne State University, USA
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.
Citation:
Manjeet Rege, Ming Dong, Farshad Fotouhi, "Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning," icdm, pp.532-541, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006
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