A Rough Set Approach to Attribute Generalization in Data Mining

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. Q 1998 Elsevier Science Inc. All rights reserved.

• Type: Journal
• Year: 1998
• Where: ISCI
• Authors: Chien-Chung Chan
• Keywords: Dynamic Conceptual Hierarchies, Inductive Learning Algorithm, ISCI 1998, Large Data Bases