CACTUS—clustering categorical data using summaries
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ROCK: a robust clustering algorithm for categorical attributes
Information Systems
COOLCAT: an entropy-based algorithm for categorical clustering
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Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
GPU-Based biclustering for neural information processing
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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Newman and Girvan [12] recently proposed an objective function for graph clustering called the Modularity function which allows automatic selection of the number of clusters. Empirically, higher values of the Modularity function have been shown to correlate well with good graph clustering. In this paper we propose an extended Modularity measure for categorical data clustering; first, we establish the connection with the Relational Analysis criterion. The proposed Modularity measure introduces an automatic weighting scheme which takes in consideration the profile of each data object. A modified Relational Analysis algorithm is then presented to search for the partitions maximizing the criterion. This algorithm deals linearly with large data set and allows natural clusters identification, i.e. doesn't require fixing the number of clusters and size of each cluster. Experimental results indicate that the new algorithm is efficient and effective at finding both good clustering and the appropriate number of clusters across a variety of real-world data sets.