Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Block clustering with Bernoulli mixture models: Comparison of different approaches
Computational Statistics & Data Analysis
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The modularity measure have been recently proposed for graph clustering which allows automatic selection of the number of clusters. Empirically, higher values of the modularity measure have been shown to correlate well with graph clustering. In order to tackle the co-clustering problem for binary data, we propose a generalized modularity measure and a spectral approximation of the modularity matrix. A spectral algorithm maximizing the modularity measure is then presented to search for the row and column clusters simultaneously. Experimental results are performed on a variety of real-world data sets confirming the interest of the use of the modularity.