2013 IEEE 13th International Conference on Data Mining (2006)
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.36
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.
Manjeet Rege, Ming Dong, Farshad Fotouhi, "Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 532-541, 2006, doi:10.1109/ICDM.2006.36