KNOWLEDGE REDUCTION IN DATABASE SYSTEMS
In the Rough Set model based on database systems, the algorithms of calculating cores and reducts are very efficient and scalable in data mining applications. However, one serious drawback of these algorithms is that they are only applicable for consistent decision tables. In this paper we propose an algorithm which is able to receive consistent decision tables by initial decision tables while at same time preserve both cores and reducts, thus overcome this drawback. By utilizing set-oriented operations, this algorithm is rewritten in database systems. Analysis shows that calculating cores and reducts of inconsistent decision tables is still efficient and scalable when this rewritten version is employed.
Rough set theory decision tables knowledge reduction database systems
QI-HE LIU HONG-BIN CAI MING-YUN HE YING QIAO
College of Computer Science and Engineering, University of Electronic Science and Technology of Chin College of Computer Science, Southwest Petroleum University, Chengdu, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2278-2283
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）