Co-clustering documents and words using Bipartite Spectral Graph Partitioning
page 269--274. (2001)Abstract
Both document clustering and word clustering are important and well-studied problems. By using the vector space model, a document collection may be represented as a word-document matrix. In this paper, we present the novel idea of modeling the document collection as a bipartite graph between documents and words. Using this model, we pose the clustering probliem as a graph partitioning problem and give a new spectral algorithm that simultaneously yields a clustering of documents and words. This co-clustrering algorithm uses the second left and right singular vectors of an appropriately scaled word-document matrix to yield good bipartitionings. In fact, it can be shown that these singular vectors give a real relaxation to the optimal solution of the graph bipartitioning problem. We present several experimental results to verify that the resulting co-clustering algoirhm works well in practice and is robust in the presence of noise.
Description
Co-clustering documents and words using Bipartite Spectral Graph Partitioning
