Data Mining and Knowledge Discovery

, Volume 28, Issue 2, pp 304–336 | Cite as

Adaptive evolutionary clustering



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 traditional 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 static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. 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.


Evolutionary clustering Similarity measures Clustering algorithms Tracking Data smoothing Adaptive filtering Shrinkage estimation 


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Copyright information

© The Author(s) 2013

Authors and Affiliations

  • Kevin S. Xu
    • 1
  • Mark Kliger
    • 2
  • Alfred O. Hero III
    • 1
  1. 1.EECS DepartmentUniversity of MichiganAnn ArborUSA
  2. 2.Omek InteractiveBet ShemeshIsrael

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