Bi-clustering gene expression data using co-similarity
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A semantic similarity measure in document databases: an earth mover's distance-based approach
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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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