Clustering Categorical Data via Ensembling Dissimilarity Matrices

Saeid Amiri, Bertrand S. Clarke, Jennifer L. Clarke

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We present a technique for clustering categorical data by generating many dissimilarity matrices and combining them. We begin by demonstrating our technique on low-dimensional categorical data and comparing it to several other techniques that have been proposed. We show through simulations and examples that our method is both more accurate and more stable. Then we give conditions under which our method should yield good results in general. Our method extends to high-dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables. In this context, we compare our method with two other methods. Finally, we extend our method to high-dimensional categorical data vectors of unequal length by using alignment techniques to equalize the lengths. We give an example to show that our method continues to provide useful results, in particular, providing a comparison with phylogenetic trees. Supplementary material for this article is available online.

Original languageEnglish (US)
Pages (from-to)195-208
Number of pages14
JournalJournal of Computational and Graphical Statistics
Issue number1
StatePublished - Jan 2 2018


  • Categorical data
  • Classification and clustering
  • Hamming distance
  • High-dimensional data
  • Sequence alignment
  • Stability

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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