Algorithms for clustering data
Algorithms for clustering data
A fast parametric maximum flow algorithm and applications
SIAM Journal on Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Improving graph partitions using submodular functions
Discrete Applied Mathematics - Submodularity
Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
An algorithm for improving graph partitions
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Submodular fractional programming for balanced clustering
Pattern Recognition Letters
Proceedings of the forty-third annual ACM symposium on Theory of computing
A class of submodular functions for document summarization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Word alignment via submodular maximization over matroids
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Promoting diversity in recommendation by entropy regularizer
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 [8] 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.