Propositional satisfiability algorithm to find minimal reducts for data mining

Azuraliza Abu Bakar, M. N. Sulaiman, M. Othman, M. H. Selamat

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A fundamental problem in data mining is whether the whole information available is always necessary to represent the information system(TS). Reduct is a rough set approach in data mining that determines the set of important attributes to represent the IS. The search for minimal reduct is based on the assumption that within the dataset in an IS, there are attributes that are more important than the rest. An algorithm in finding minimal reducts based on Prepositional Satisfiability (SAT) algorithm is proposed. A branch and bound algorithm is presented to solve the proposed SAT problem. The experimental result shows that the proposed algorithm has significantly reduced the number of rules generated from the obtained reducts with high percentage of classification accuracy.

Original languageEnglish
Pages (from-to)379-389
Number of pages11
JournalInternational Journal of Computer Mathematics
Volume79
Issue number4
DOIs
Publication statusPublished - 2002
Externally publishedYes

Fingerprint

Reduct
Data mining
Data Mining
Attribute
Satisfiability Problem
Branch and Bound Algorithm
Rough Set
Percentage
Information Systems
Information systems
Necessary
Experimental Results

Keywords

  • Binary Integer Programming(BIP)
  • Conjunctive Normal Forms (CNF)
  • Data Mining
  • Prepositional Satisfiability (SAT)
  • Reduct
  • Rough Set

ASJC Scopus subject areas

  • Applied Mathematics

Cite this

Propositional satisfiability algorithm to find minimal reducts for data mining. / Abu Bakar, Azuraliza; Sulaiman, M. N.; Othman, M.; Selamat, M. H.

In: International Journal of Computer Mathematics, Vol. 79, No. 4, 2002, p. 379-389.

Research output: Contribution to journalArticle

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