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EnglishThe 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.
An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects / CHIARAVALLOTI A., D; Greco, Gianluigi; Guzzo, Antonella; Pontieri, L.. - 4212(2006), pp. 598-605. ((Intervento presentato al convegno 17th European Conference on Machine Learning (ECML'06) tenutosi a Berlin, Germany nel September 18-22, 2006.
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| Titolo: | An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects |
| Autori: | |
| Data di pubblicazione: | 2006 |
| Rivista: | |
| Citazione: | An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects / CHIARAVALLOTI A., D; Greco, Gianluigi; Guzzo, Antonella; Pontieri, L.. - 4212(2006), pp. 598-605. ((Intervento presentato al convegno 17th European Conference on Machine Learning (ECML'06) tenutosi a Berlin, Germany nel September 18-22, 2006. |
| Handle: | http://hdl.handle.net/20.500.11770/180215 |
| ISBN: | 3-540-45375-X |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/20.500.11770/180215
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