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

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 pape...

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Bibliographic Details
Main Authors: Bisson, G., Hussain, F.
Format: Conference Proceeding
Language:English
Subjects:
Bioinformatics
Clustering algorithms
Clustering methods
Co-clustering
co-similarity
Current measurement
Gene expression
Iterative algorithms
Machine learning
Organizing
Sparse matrices
Spatial databases
text mining
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Summary: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.
DOI:10.1109/ICMLA.2008.103