Unsupervised random forests

Alejandro Mantero, Hemant Ishwaran

Research output: Contribution to journalArticlepeer-review

Abstract

sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID interaction features). Then a multivariate random forest (able to handle both continuous and categorical variables) is used to predict the SID main features. We establish uniqueness of sidification and show how multivariate impurity splitting is able to identify clusters. The proposed sidClustering method is adept at finding clusters arising from categorical and continuous variables and retains all the important advantages of random forests. The method is illustrated using simulated and real data as well as two in depth case studies, one from a large multi-institutional study of esophageal cancer, and the other involving hospital charges for cardiovascular patients.

Original languageEnglish (US)
Pages (from-to)144-167
Number of pages24
JournalStatistical Analysis and Data Mining
Volume14
Issue number2
DOIs
StatePublished - Apr 2021

Keywords

  • impurity
  • sidClustering
  • staggered interaction data
  • unsupervised learning

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

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