Hierarchical co-clustering based on entropy splitting
Wei Dong Cheng, Xiang Zhang, Feng Pan, Wei Wang · 2012
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.