Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
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
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Building implicit links from content for forum search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Quantify music artist similarity based on style and mood
Proceedings of the 10th ACM workshop on Web information and data management
Parameter-Free Hierarchical Co-clustering by n-Ary Splits
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Hierarchical co-clustering for web queries and selected URLs
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
HCC: a hierarchical co-clustering algorithm
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Two dimensional contingency tables or co-occurrence matrices arise frequently in various important applications such as text analysis and web-log mining. As a fundamental research topic, co-clustering aims to generate a meaningful partition of the contingency table to reveal hidden relationships between rows and columns. Traditional co-clustering algorithms usually produce a predefined number of flat partition of both rows and columns, which do not reveal relationship among clusters. To address this limitation, hierarchical co-clustering algorithms have attracted a lot of research interests recently. Although successful in various applications, the existing hierarchial co-clustering algorithms are usually based on certain heuristics and do not have solid theoretical background. In this paper, we present a new co-clustering algorithm with solid information theoretic background. It simultaneously constructs a hierarchical structure of both row and column clusters which retains sufficient mutual information between rows and columns of the contingency table. An efficient and effective greedy algorithm is developed which grows a co-cluster hierarchy by successively performing row-wise or column-wise splits that lead to the maximal mutual information gain. Extensive experiments on real datasets demonstrate that our algorithm can reveal essential relationships of row (and column) clusters and has better clustering precision than existing algorithms.