Automatic subspace clustering of high dimensional data for data mining applications
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PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Hierarchical co-clustering: off-line and incremental approaches
Data Mining and Knowledge Discovery
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We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by clustering tuples in a dataset, where each cluster is characterized by a different probability distribution. Subsequently, the conditional distribution of attributes over tuples is exploited to discover natural co-clusters in the data. An intensive empirical evaluation highlights the effectiveness of our approach.