Adaptive Evolutionary Clustering

Details Adaptive Evolutionary Clustering Adaptive Evolutionary Clustering

2011-04-11

arxiv.org/abs/1104.1990v1

Computer Science

Learning

29 pages, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affect

Scientific paper

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms ordinary static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by ordinary static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using a shrinkage approach. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.

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