Soft Techniques to Data Mining

Abstract

This paper describes two soft techniques, GDT-NN and GDT-RS, for mining if-then rules in databases with uncertainty and incompleteness. The techniques are based on a Generalization Distribution Table (GDT), in which the probabilistic relationships between concepts and instances over discrete domains are represented. The GDT provides a probabilistic basis for evaluating the strength of a rule. We describe that a GDT can be represented by connectionist networks (GDT-NN for short), and if-then rules can be discovered by learning on the GDT-NN. Further-more, we combine the GDT with the rough set methodology (GDT-RS for short). Thus, we can first find the rules with larger strengths from possible rules, and then find minimal relative reducts from the set of rules with larger strengths. The strength of a rule represents the uncertainty of the rule, which is influenced by both unseen instances and noises. We compare GDT-NN with GDT-RS, and describe GDT-RS is a better way than GDT-NN for large, complex databases.