A rough set approach to attribute generalization in data mining

被引：104
|
作者：
Chan, CC [1 ]
机构：
[1] Univ Akron, Dept Math Sci, Akron, OH 44325 USA
来源：
INFORMATION SCIENCES | 1998年 / 107卷 / 1-4期
关键词：
rough sets; data mining; inductive learning;
D O I：
10.1016/S0020-0255(97)10047-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory. (C) 1998 Elsevier Science Inc. All rights reserved.
引用
收藏
页码：169 / 176
页数：8
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