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Cluster Forests

Author

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  • Yan, Donghui
  • Chen, Aiyou
  • Jordan, Michael I.

Abstract

With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method—Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain “good local clusterings” and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure kappa. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis reveals that the kappa measure makes it possible to grow the local clustering in a desirable way—it is “noise-resistant”. A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.

Suggested Citation

  • Yan, Donghui & Chen, Aiyou & Jordan, Michael I., 2013. "Cluster Forests," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 178-192.
    • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:178-192
    DOI: 10.1016/j.csda.2013.04.010
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    References listed on IDEAS

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    1. Nowicki K. & Snijders T. A. B., 2001. "Estimation and Prediction for Stochastic Blockstructures," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1077-1087, September.
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    Cited by:

    1. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.

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