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Attribute‐oriented fuzzy generalization in proximity‐ and similarity‐based relational database systems
Author(s) -
Angryk Rafal A.,
Petry Frederick E.
Publication year - 2007
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20227
Subject(s) - computer science , data mining , relational database , relation (database) , fuzzy logic , schema (genetic algorithms) , database schema , equivalence relation , database , database design , information retrieval , artificial intelligence , mathematics , discrete mathematics
In this article we investigate an attribute‐oriented induction approach for acquisition of abstract knowledge from data stored in a fuzzy database environment. We utilize a proximity‐based fuzzy database schema as the medium carrying the original information, where lack of precise information about an entity can be reflected via multiple attribute values, and the classical equivalence relation is replaced with the broader fuzzy proximity relation. We analyze in detail the process of attribute‐oriented induction by concept hierarchies, utilizing the original properties of fuzzy databases to support this established data mining technique. In our approach we take full advantage of the implicit knowledge about the similarity of original attribute values, included by default in the investigated fuzzy database schemas. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 763–779, 2007.