Approximate reasoning applied to unsupervised database mining
Lawrence J. Mazlack,
Corresponding Author
Lawrence J. Mazlack
Computer Science Area, ECECS Department, University of Cincinnati, Cincinnati, OH 45221-0030
Computer Science Area, ECECS Department, University of Cincinnati, Cincinnati, OH 45221-0030Search for more papers by this authorLawrence J. Mazlack,
Corresponding Author
Lawrence J. Mazlack
Computer Science Area, ECECS Department, University of Cincinnati, Cincinnati, OH 45221-0030
Computer Science Area, ECECS Department, University of Cincinnati, Cincinnati, OH 45221-0030Search for more papers by this authorFirst published: 07 December 1998
Citations: 2
Abstract
A computational approach is shown for unsupervised, reactive, database mining. This approach is dependent on soft computing techniques. Database mining seeks to discover noteworthy, unrecognized associations between database items. A novel approach is suggested for unsupervised search controlled by dissonance reduction. Both crisp and noncrisp data are subject to discovery. Another aspect of uncertainty is the metric that controls discovery. Issues involve: coherence measures, granularization, user intelligible results, unsupervised recognition of interesting results, and concept equivalent formation. © 1997 John Wiley & Sons, Inc.
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