Propositional satisfiability algorithm to find minimal reducts for data mining

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 th...

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Main Authors: Bakar, A.A., Sulaiman, M.N., Othman, M., Selamat, M.H.
Format: Article
Published: 2002
Online Access:http://psasir.upm.edu.my/id/eprint/116270/
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author Bakar, A.A.
Sulaiman, M.N.
Othman, M.
Selamat, M.H.
author_facet Bakar, A.A.
Sulaiman, M.N.
Othman, M.
Selamat, M.H.
author_sort Bakar, A.A.
building UPM Institutional Repository
collection Online Access
description 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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spelling upm-1162702025-03-24T04:50:30Z http://psasir.upm.edu.my/id/eprint/116270/ Propositional satisfiability algorithm to find minimal reducts for data mining Bakar, A.A. Sulaiman, M.N. Othman, M. Selamat, M.H. 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. 2002 Article PeerReviewed 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 https://www.tandfonline.com/doi/abs/10.1080/00207160210938 10.1080/00207160210938
spellingShingle Bakar, A.A.
Sulaiman, M.N.
Othman, M.
Selamat, M.H.
Propositional satisfiability algorithm to find minimal reducts for data mining
title Propositional satisfiability algorithm to find minimal reducts for data mining
title_full Propositional satisfiability algorithm to find minimal reducts for data mining
title_fullStr Propositional satisfiability algorithm to find minimal reducts for data mining
title_full_unstemmed Propositional satisfiability algorithm to find minimal reducts for data mining
title_short Propositional satisfiability algorithm to find minimal reducts for data mining
title_sort propositional satisfiability algorithm to find minimal reducts for data mining
url http://psasir.upm.edu.my/id/eprint/116270/
http://psasir.upm.edu.my/id/eprint/116270/
http://psasir.upm.edu.my/id/eprint/116270/