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Evidence accumulation clustering using combinations of features

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  • Wong, William
  • Tsuchiya, Naotsugu

Abstract

Evidence accumulation clustering (EAC) is an ensemble clustering algorithm that can cluster data for arbitrary shapes and numbers of clusters. Here, we present a variant of EAC in which we aimed to better cluster data with a large number of features, many of which may be uninformative. Our new method builds on the existing EAC algorithm by populating the clustering ensemble with clusterings based on combinations of fewer features than the original dataset at a time. Our method also calls for prewhitening the recombined data and weighting the influence of each individual clustering by an estimate of its informativeness. We provide code of an example implementation of the algorithm in Matlab and demonstrate its effectiveness compared to ordinary evidence accumulation clustering with synthetic data.

Suggested Citation

  • Wong, William & Tsuchiya, Naotsugu, 2020. "Evidence accumulation clustering using combinations of features," OSF Preprints epb6t, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:epb6t
    DOI: 10.31219/osf.io/epb6t
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    References listed on IDEAS

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    1. Agnan Kessy & Alex Lewin & Korbinian Strimmer, 2018. "Optimal Whitening and Decorrelation," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 309-314, October.
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