DocumentCode :
3027185
Title :
Softly focusing on data
Author :
Mazlack, Lawrence J.
Author_Institution :
Dept. of Comput. Sci., Cincinnati Univ., OH, USA
fYear :
1999
fDate :
36342
Firstpage :
700
Lastpage :
704
Abstract :
A computational approach providing a focus for unsupervised, reactive data mining is suggested. In data mining, achieving focus is an important issue. This is because there are too many attributes and values in a real database to consider them all. A soft focus is suggested, as both the data and the focus product may be imprecise. An approach is suggested for unsupervised searching controlled by progressive reduction of cognitive dissonance. Both crisp and non-crisp data are subject to discovery. Soft completing tools are needed because of the need to granulize data and to establish crisp boundaries in a non-crisp world. Issues involve: coherence measures, granularization, user-intelligible results, unsupervised recognition of interesting results, and concept-equivalent formation
Keywords :
data mining; data reduction; database theory; fuzzy logic; pattern clustering; search problems; uncertainty handling; unsupervised learning; cognitive dissonance progressive reduction; coherence measures; concept-equivalent formation; crisp boundaries; crisp data; data discovery; data focus; data granularization; data values; database attributes; fuzzy data; interesting results; noncrisp data; soft completing; soft focus; unsupervised reactive data mining; unsupervised recognition; unsupervised searching; user-intelligible results; Computer science; Credit cards; Data mining; Humans; Information retrieval; Investments; Medical tests; Parallel algorithms; Sampling methods; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location :
New York, NY
Print_ISBN :
0-7803-5211-4
Type :
conf
DOI :
10.1109/NAFIPS.1999.781784
Filename :
781784
Link To Document :
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