Hierarchical Model-Based Co-Clustering of High-Dimensional Data

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 initially clustering rows 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 co-clusters underlying the data. An intensive empirical evaluation confirms the effectiveness of our approach, even when compared to well-known co-clustering schemes available from the current literature.