Fuzzy association rules for handling continuous attributes

Association rules are frequently used for describing association (i.e., co-occurrence) among attribute values in the field of data mining. When an attribute is continuous (i.e., real-valued) such as height, length and weight, its domain is usually discretized into several intervals. Fuzzy rules are recognized as a convenient tool for handling continuous attributes in a human understandable manner. When we use fuzzy rules, the domain of each continuous attribute is discretized into several fuzzy sets. A linguistic label is usually associated with each fuzzy set especially when linguistic interpretations of fuzzy rules are required. In this paper, we first fuzzify the concept of association rules. That is, we show fuzzy versions of two measures (i.e., confidence and support) that are used for evaluating each association rule in the field of data mining. Then we illustrate these two measures of fuzzy rules for function approximation and pattern classification problems. Finally we examine the relation between the classification performance of fuzzy rules and the definition of their certainty grades through computer simulations. Simulation results show that the direct use of confidence as a certainty grade is not always appropriate from the viewpoint of classification performance.