Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
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The high-order co-clustering problem, i.e., the problem of simultaneously clustering several heterogeneous types of domains, is usually faced by minimizing a linear combination of some optimization functions evaluated over pairs of correlated domains, where each weight expresses the reliability/relevance of the associated contingency table. Clearly enough, accurately choosing these weights is crucial to the effectiveness of the co-clustering, and techniques for their automatic tuning are particularly desirable, which are instead missing in the literature. This paper faces this issue by proposing an information-theoretic framework where the co-clustering problem does not need any explicit weighting scheme for combining pairwise objective functions, while a suitable notion of agreement among these functions is exploited. Based on this notion, an algorithm for co-clustering a “star-structured” collection of domains is defined.