An Information-Theoretic Framework for High-Order Co-Clustering of Heterogeneous Objects
Abstract
The 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.
Cite
Text
Chiaravalloti et al. "An Information-Theoretic Framework for High-Order Co-Clustering of Heterogeneous Objects." European Conference on Machine Learning, 2006. doi:10.1007/11871842_57Markdown
[Chiaravalloti et al. "An Information-Theoretic Framework for High-Order Co-Clustering of Heterogeneous Objects." European Conference on Machine Learning, 2006.](https://mlanthology.org/ecmlpkdd/2006/chiaravalloti2006ecml-informationtheoretic/) doi:10.1007/11871842_57BibTeX
@inproceedings{chiaravalloti2006ecml-informationtheoretic,
title = {{An Information-Theoretic Framework for High-Order Co-Clustering of Heterogeneous Objects}},
author = {Chiaravalloti, Antonio D. and Greco, Gianluigi and Guzzo, Antonella and Pontieri, Luigi},
booktitle = {European Conference on Machine Learning},
year = {2006},
pages = {598-605},
doi = {10.1007/11871842_57},
url = {https://mlanthology.org/ecmlpkdd/2006/chiaravalloti2006ecml-informationtheoretic/}
}