DocumentCode
304104
Title
Database mining by learned cognitive dissonance reduction
Author
Mazlack, Lawrence J.
Author_Institution
ECECS Dept., Cincinnati Univ., OH, USA
Volume
2
fYear
1996
fDate
8-11 Sep 1996
Firstpage
1506
Abstract
A soft computing approach for unsupervised, reactive, database mining is used. This is to meet the broad goals of database mining of discovering noteworthy, unrecognized associations between database items. A novel approach is suggested for unsupervised search controlled by dissonance reduction. Both crisp and non-crisp data are subject to discovery. Issues involve: coherence measures, granularization, user intelligible results, unsupervised recognition of interesting results, and concept equivalent formation
Keywords
inference mechanisms; knowledge acquisition; knowledge based systems; query processing; unsupervised learning; coherence measures; concept equivalent formation; crisp data; database mining; granularization; learned cognitive dissonance reduction; noncrisp data; soft computing; unsupervised reactive database mining; unsupervised recognition; unsupervised search; user intelligible results; Computer science; Data mining; Databases; Humans; Information theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3645-3
Type
conf
DOI
10.1109/FUZZY.1996.552398
Filename
552398
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