Affiliations: Department of Information Management, National Central University, Chung-Li, Taiwan, Republic of China
Correspondence:
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Corresponding author. Yen-Liang Chen, Department of Information Management, National Central University, Chung-Li, Taiwan 320, Republic of China. Tel.: +886 3 4267266; Fax: +886 3 425 4604; E-mail: ylchen@mgt.ncu.edu.tw.
Abstract: In the rough set theory proposed by Pawlak, the concept of reduct is very important. The reduct is the minimum attribute set that preserves the partition of the universe. A great deal of research in the past has attempted to reduce the representation of the original table. The advantage of using a reduced representation table is that it can summarize the original table so that it retains the original knowledge without distortion. However, using reduct to summarize tables may encounter the problem of the table still being too large, so users will be overwhelmed by too much information. To solve this problem, this article considers how to further reduce the size of the table without causing too much distortion to the original knowledge. Therefore, we set an upper limit for information distortion, which represents the maximum degree of information distortion we allow. Under this upper limit of distortion, we seek to find the summary table with the highest compression. This paper proposes two algorithms. The first is to find all summary tables that satisfy the maximum distortion constraint, while the second is to further select the summary table with the greatest degree of compression from these tables.
Keywords: Rough set, reduct, attribute reduction, information system, summarization
