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Evolutionary soft co-clustering: formulations, algorithms, and applications

 

Wenlu Zhang, Rongjian Li, Daming Feng, Andrey Chernikov, Nikos Chrisochoides, Christopher Osgood and Shuiwang Ji.

 

Published in Data Mining and Knowledge Discovery, Publisher Springer, DOI 10.1007/s10618-014-0375-9, August, 2014

 

Abstract

 

We consider the co-clustering of time-varying data using evolutionary coclustering methods. Existing approaches are based on the spectral learning framework, thus lacking a probabilistic interpretation.We overcome this limitation by developing a probabilistic model in this paper. The proposed model assumes that the observed data are generated via a two-step process that depends on the historic co-clusters. This allows us to capture the temporal smoothness in a probabilistically principled manner. To perform maximum likelihood parameter estimation, we present an EM-based algorithm. We also establish the convergence of the proposed EM algorithm. An appealing feature of the proposed model is that it leads to soft co-clustering assignments naturally. We evaluate the proposed method on both synthetic and real-world data sets.