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Chi-Sim: A New Similarity Measure for the Co-clustering Task

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2 Author(s)
Bisson, G. ; Lab. TIMC-IMAG, Univ. de Grenoble, La Tronche ; Hussain, F.

Co-clustering has been widely studied in recent years. Exploiting the duality between objects and features efficiently helps in better clustering both objects and features. In contrast with current co-clustering algorithms that focus on directly finding some patterns in the data matrix, in this paper we define a (co-)similarity measure, named X-Sim, which iteratively computes the similarity between objects and their features. Thus, it becomes possible to use any clustering methods (k-means, ...) to co-cluster data. The experiments show that our algorithm not only outperforms the classical similarity measure but also outperforms some co-clustering algorithms on the document-clustering task.

Published in:

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Date of Conference:

11-13 Dec. 2008