In many real-world applications that analyze correlations between two groups of diverse entities, each group of entities can
characterized by multiple attributes. As such, there is a need to co-cluster multiple attributes’ values into pairs of highly correlated clusters. We denote this co-clustering problem as the multi-attribute co-clustering problem. In this paper, we introduce a generalization of the mutual information between two attributes into mutual informationbetween two attribute sets. The generalized formula enables us to use correlation information to discover multi-attribute co-clusters (MACs). We develop a novel algorithm MACminer to mine MACs with high correlation information from datasets. We demonstrate the miningefficiency of MACminer in datasets with multiple attributes, and show that MACs with high correlation information have higherclassification and predictive power, as compared to MACs generated by alternative high-dimensional data clustering and patternmining techniques.

@article{kelvin2009multiattribute, author = {Sim, Kelvin and Gopalkrishnan, Vivekanand and Chua, Hon and Ng, See-Kiong}, title = {MACs: Multi-Attribute Co-clusters with High Correlation Information}, journal = {Machine Learning and Knowledge Discovery in Databases}, year = {2009}, pages = {398--413}, url = {http://dx.doi.org/10.1007/978-3-642-04174-7_26}, keywords = {2009, clustering, co-clustering, ecml, pkdd}, abstract = {In many real-world applications that analyze correlations between two groups of diverse entities, each group of entities can be characterized by multiple attributes. As such, there is a need to co-cluster multiple attributes’ values into pairs of highly correlated clusters. We denote this co-clustering problem as the multi-attribute co-clustering problem. In this paper, we introduce a generalization of the mutual information between two attributes into mutual informationbetween two attribute sets. The generalized formula enables us to use correlation information to discover multi-attribute co-clusters (MACs). We develop a novel algorithm MACminer to mine MACs with high correlation information from datasets. We demonstrate the miningefficiency of MACminer in datasets with multiple attributes, and show that MACs with high correlation information have higherclassification and predictive power, as compared to MACs generated by alternative high-dimensional data clustering and patternmining techniques.} }

%0 = article %A = Sim, Kelvin and Gopalkrishnan, Vivekanand and Chua, Hon and Ng, See-Kiong %D = 2009 %T = MACs: Multi-Attribute Co-clusters with High Correlation Information %U = http://dx.doi.org/10.1007/978-3-642-04174-7_26

@inproceedings{conf/icdm/ShanB08, author = {Shan, Hanhuai and Banerjee, Arindam}, title = {Bayesian Co-clustering.}, booktitle = {ICDM}, publisher = {IEEE Computer Society}, year = {2008}, pages = {530-539}, url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#ShanB08}, keywords = {bayesian, clustering, co-clustering, lda} }

%0 = inproceedings %A = Shan, Hanhuai and Banerjee, Arindam %B = ICDM %D = 2008 %I = IEEE Computer Society %T = Bayesian Co-clustering. %U = http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#ShanB08

@inproceedings{shafiei_milios06, author = {Shafiei, M. Mahdi and Milios, Evangelos E.}, title = {Latent Dirichlet Co-Clustering}, booktitle = {ICDM '06: Proceedings of the Sixth International Conference on Data Mining}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, year = {2006}, pages = {542--551}, doi = {http://dx.doi.org/10.1109/ICDM.2006.94}, isbn = {0-7695-2701-9}, keywords = {clustering, co-clustering, lda, models, topic} }

%0 = inproceedings %A = Shafiei, M. Mahdi and Milios, Evangelos E. %B = ICDM '06: Proceedings of the Sixth International Conference on Data Mining %C = Washington, DC, USA %D = 2006 %I = IEEE Computer Society %T = Latent Dirichlet Co-Clustering

@inproceedings{dhillon:mallela:modha:03, author = {Dhillon, I. S. and Mallela, S. and Modha, D. S.}, title = {Information-Theoretic Co-Clustering}, booktitle = {Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2003)}, year = {2003}, pages = {89--98}, url = {/brokenurl#citeseer.ist.psu.edu/dhillon03informationtheoretic.html}, keywords = {clustering, co-clustering, dhillon, information, theory, toread} }

%0 = inproceedings %A = Dhillon, I. S. and Mallela, S. and Modha, D. S. %B = Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2003) %D = 2003 %T = Information-Theoretic Co-Clustering %U = /brokenurl#citeseer.ist.psu.edu/dhillon03informationtheoretic.html