Local Search for Balanced Submodular Clusterings
Mukund Narasimhan, Jeff Bilmes
In this paper, we consider the problem of producing balanced clusterings with respect to a submodular objective function. Submodular objective functions occur frequently in many applications, and hence this problem is broadly applicable. We show that the results of Patkar and Narayanan (2003) can be applied to cases when the submodular function is derived from a bipartite object-feature graph, and moreover, in this case we have an efficient flow based algorithm for finding local improvements. We show the effectiveness of this approach by applying it to the clustering of words in language models.