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 documentword bipartite graph.
Date of creation, presentation, or exhibit
Department, Program, or Center
Computer Science (GCCIS)
Co-clustering documents and words using Bipartite Isoperimetric Graph Partitioning, Proceedings of the IEEE International Conference on Data Mining (ICDM) 2006. Held in Hong Kong.
RIT – Main Campus