Co-clustering Documents and Words by Minimizing the Normalized Cut Objective Function

This paper follows a word-document co-clustering model independently introduced in 2001 by several authors such as I.S. Dhillon, H. Zha and C. Ding. This model consists in creating a bipartite graph based on word frequencies in documents, and whose vertices are both documents and words. The created bipartite graph is then partitioned in a way that minimizes the normalized cut objective function to produce the document clustering. The fusion-fission graph partitioning metaheuristic is applied on several document collections using this word-document co-clustering model. Results demonstrate a real problem in this model: partitions found almost always have a normalized cut value lowest than the original document collection clustering. Moreover, measures of the goodness of solutions seem to be relatively independent of the normalized cut values of partitions.