Finding minimal reduct with binary integer programming in data mining

Author

Bakar, Afarulrazi Abu ; Sulaiman, Md Nasir ; Othman, Mohamed ; Selamat, M.H.

Author_Institution

Fac. of Comput. Sci. & Inf. Technol., Univ. Putra Malaysia, Selangor, Malaysia

Volume

3

fYear

2000

fDate

2000

Firstpage

141

Abstract

The search for the minimum size of reduct is based on the assumption that, within the data set, there are some attributes that are more important than the rest. In this paper, we present an algorithm for finding minimum-size reducts which is based on a rough set approach and a dedicated decision-related binary integer programming (BIP) algorithm. The algorithm transforms an equivalence class obtained from a decision system into a BIP model. An algorithm for solving the BIP is given. The presented work has links to rough set theory, data mining and nonmonotonic reasoning

Keywords

data mining; database theory; decision theory; equivalence classes; integer programming; minimisation; nonmonotonic reasoning; rough set theory; attribute importance; conjunctive normal form formula; data mining; decision system; dedicated decision-related binary integer programming algorithm; equivalence class; minimal reduct search; nonmonotonic reasoning; prime implicant; rough set theory; Algorithm design and analysis; Artificial intelligence; Computer science; Data mining; Databases; Information systems; Linear programming; Logic design; Machine learning algorithms; Set theory;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2000. Proceedings

Conference_Location

Kuala Lumpur

Print_ISBN

0-7803-6355-8

Type

conf

DOI

10.1109/TENCON.2000.892239

Filename

892239