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- W2102774914 abstract "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." @default.
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- W2102774914 date "2012-10-29" @default.
- W2102774914 modified "2023-09-27" @default.
- W2102774914 title "Hierarchical co-clustering based on entropy splitting" @default.
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- W2102774914 doi "https://doi.org/10.1145/2396761.2398455" @default.
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