Evidence accumulation clustering using combinations of features

William Wong, Naotsugu Tsuchiya

Research output: Contribution to journalArticleOtherpeer-review

1 Citation (Scopus)

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. • The clustering ensemble is made by clustering on subset combinations of features from the data • The recombined data may be prewhitened • Evidence accumulation can be improved by weighting the evidence with a goodness-of-clustering measure

Original languageEnglish
Article number100916
Number of pages19
JournalMethodsX
Volume7
DOIs
Publication statusPublished - 2020

Keywords

  • Combination clustering
  • Combinatorial evidence accumulation clustering
  • Ensemble clustering
  • k-means clustering

Cite this