APPROXIMATE REASONING APPLIED TO UNSUPERVISED DATABASE MINING

A computational approach is shown for unsupervised, reactive, database mining. This approach is dependent on soft computing techniques. Data base mining seeks to discover noteworthy, unrecognized associations be tween database items. A novel approach is suggested for unsupervised s earch 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, granular ization, user intelligible results, unsupervised recognition of intere sting results, and concept equivalent formation. (C) 1997 John Wiley & Sons, Inc.