%0 Conference Paper %1 Dhillon03InformationCoclustering %A Dhillon, Inderjit S. %A Mallela, Subramanyam %A Modha, Dharmendra S. %B KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2003 %I ACM %K 03 Dhillon clustering coclustering information text theory %P 89--98 %R http://doi.acm.org/10.1145/956750.956764 %T Information-theoretic co-clustering %U http://portal.acm.org/citation.cfm?id=956764 %X Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory---the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters. We present an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages. Using the practical example of simultaneous word-document clustering, we demonstrate that our algorithm works well in practice, especially in the presence of sparsity and high-dimensionality. %@ 1-58113-737-0

@inproceedings{Dhillon03InformationCoclustering, abstract = {Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory---the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters. We present an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages. Using the practical example of simultaneous word-document clustering, we demonstrate that our algorithm works well in practice, especially in the presence of sparsity and high-dimensionality.}, added-at = {2008-11-13T19:18:30.000+0100}, address = {New York, NY, USA}, author = {Dhillon, Inderjit S. and Mallela, Subramanyam and Modha, Dharmendra S.}, biburl = {https://www.bibsonomy.org/bibtex/2108f77a4e13823f24497340ff1cba0ad/lee_peck}, booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining}, description = {Information-theoretic co-clustering}, doi = {http://doi.acm.org/10.1145/956750.956764}, interhash = {30fe4c22011ee3c5565d35709d9ce1f1}, intrahash = {108f77a4e13823f24497340ff1cba0ad}, isbn = {1-58113-737-0}, keywords = {03 Dhillon clustering coclustering information text theory}, location = {Washington, D.C.}, pages = {89--98}, publisher = {ACM}, timestamp = {2009-02-02T16:40:19.000+0100}, title = {Information-theoretic co-clustering}, url = {http://portal.acm.org/citation.cfm?id=956764}, year = 2003 }