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