Rule Eztraction from Incomplete Decision Tables
Rule extraction is an important issue of data mining and many efficient algorithms based on rough sets have been presented for obtaining rules from decision tables. However, little work has been focused on extracting rules from the incomplete decision tables. In this paper based on an improved discernibility matrix an efficient method for obtaining all optimal credible decision rules from an incomplete decision table is proposed. Through uniting the objects of a maximal tolerance class into a new object the scale of discernibility matrix used to produce the disjunction of rules is greatly reduced, and then the computation efficiency of the rule extraction gets an obvious improvement. Theoretical analysis and experiments indicate that the improved method is more efficient for obtaining optimal credible decision rules from an incomplete decision tables.
data mining rule eztraction incomplete decision table rough sets
Renpu Li Dedong Zhang Yongsheng Zhao Fuzeng Zhang
School of Computer Science & Technology, Ludong University, Yantai 264025, China
国际会议
2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)
太原
英文
639-642
2009-07-10（万方平台首次上网日期，不代表论文的发表时间）