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Propositional satisfiability algorithm to find minimal reducts for data mining


Citation

Bakar, A.A. and Sulaiman, M.N. and Othman, M. and Selamat, M.H. (2002) Propositional satisfiability algorithm to find minimal reducts for data mining. International Journal of Computer Mathematics, 79 (4). pp. 379-389. ISSN 1029-0265; eISSN: 0020-7160

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. © 2002 Taylor and Francis Ltd.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1080/00207160210938
Keywords: Binary Integer Programming(BIP); Conjunctive Normal Forms (CNF); Data Mining; Prepositional Satisfiability (SAT); Reduct; Rough Set
Depositing User: Ms. Azian Edawati Zakaria
Date Deposited: 24 Mar 2025 04:50
Last Modified: 24 Mar 2025 04:50
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/00207160210938
URI: http://psasir.upm.edu.my/id/eprint/116270
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