Title :
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
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;
Conference_Titel :
TENCON 2000. Proceedings
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6355-8
DOI :
10.1109/TENCON.2000.892239
