Finding "persistent rules": Combining association and classification results

Karthik Rajasethupathy, Anthony Scime, Kulathur S. Rajasethupathy, Gregory Roy Murray

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed "persistent-rules". The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice.

Original languageEnglish (US)
Pages (from-to)6019-6024
Number of pages6
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
StatePublished - Jan 1 2009
Externally publishedYes

Fingerprint

Association rules
Data mining

Keywords

  • Association mining
  • Classification
  • Persistent rules
  • Strong rules

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Finding "persistent rules" : Combining association and classification results. / Rajasethupathy, Karthik; Scime, Anthony; Rajasethupathy, Kulathur S.; Murray, Gregory Roy.

In: Expert Systems with Applications, Vol. 36, No. 3 PART 2, 01.01.2009, p. 6019-6024.

Research output: Contribution to journalArticle

Rajasethupathy, Karthik ; Scime, Anthony ; Rajasethupathy, Kulathur S. ; Murray, Gregory Roy. / Finding "persistent rules" : Combining association and classification results. In: Expert Systems with Applications. 2009 ; Vol. 36, No. 3 PART 2. pp. 6019-6024.
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