A clustering-based discretization for supervised learning

Ankit Gupta, Kishan G. Mehrotra, Chilukuri Mohan

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


We address the problem of discretization of continuous variables for machine learning classification algorithms. Existing procedures do not use interdependence between the variables towards this goal. Our proposed method uses clustering to exploit such interdependence. Numerical results show that this improves the classification performance in almost all cases. Even if an existing algorithm can successfully operate with continuous variables, better performance is obtained if the variables are first discretized. An additional advantage of discretization is that it reduces the overall computation time.

Original languageEnglish (US)
Pages (from-to)816-824
Number of pages9
JournalStatistics and Probability Letters
Issue number9-10
StatePublished - 2010


  • Binning
  • Clustering
  • Discretization
  • Supervised learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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