Dimensionality reduction via discretization

Huan Liu, Rudy Setiono

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The existence of numeric data and large numbers of records in a database present a challenging task in terms of explicit concepts extraction from the raw data. The paper introduces a method that reduces data vertically and horizontally, keeps the discriminating power of the original data, and paves the way for extracting concepts. The method is based on discretization (vertical reduction) and feature selection (horizontal reduction). The experimental results show that (a) the data can be effectively reduced by the proposed method; (b) the predictive accuracy of a classifier (C4.5) can be improved after data and dimensionality reduction; and (c) the classification rules learned are simpler.

Original languageEnglish (US)
Pages (from-to)67-72
Number of pages6
JournalKnowledge-Based Systems
Issue number1
StatePublished - Feb 1996
Externally publishedYes


  • Dimensionality reduction
  • Discretization
  • Knowledge discovery

ASJC Scopus subject areas

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Management Information Systems


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