Title :
Database mining by learned cognitive dissonance reduction
Author :
Mazlack, Lawrence J.
Author_Institution :
ECECS Dept., Cincinnati Univ., OH, USA
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;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552398
