Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised hierarchical co-clustering
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Hierarchical co-clustering based on entropy splitting
Proceedings of the 21st ACM international conference on Information and knowledge management
Combining co-clustering with noise detection for theme-based summarization
ACM Transactions on Speech and Language Processing (TSLP)
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In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering. The goal of the former theme is to organize clusters into a hierarchy that facilitates browsing and navigation, while the goal of the latter theme is to cluster different types of data simultaneously by making use of the relationship information. Our initial empirical results are promising and they demonstrate that simultaneously attempting both these goals in a single model leads to improvements over models that focus on a single goal.