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Classification with degree of membership: a fuzzy approach

Classification is an important topic in data mining research. It is concerned with the prediction of the values of some attribute in a database based on other attributes. To tackle this problem, most of the existing data mining algorithms adopt either a decision tree based approach or an approach that requires users to provide some user-specified thresholds to guide the search for interesting rules. The authors propose a new approach based on the use of an objective interestingness measure to distinguish interesting rules from uninteresting ones. Using linguistic terms to represent the revealed regularities and exceptions, this approach is especially useful when the discovered rules are presented to human experts for examination because of the affinity with the human knowledge representation. The use of a fuzzy technique allows the prediction of attribute values to be associated with degree of membership. Our approach is therefore able to deal with the cases where an object can belong to more than one class. Furthermore, our approach is more resilient to noise and missing data values because of the use of a fuzzy technique. To evaluate the performance of our approach, we tested it using several real-life databases. The experimental results show that it can be very effective at data mining tasks. When compared to popular data mining algorithms, the approach is better able to uncover useful rules hidden in databases.