FCBI: An Efficient User-Friendly Classifier Using Fuzzy Implication Table

In the past few years, exhaustive search method under the name of association rule mining has been widely used in the field of classification. However, such kind of methods usually produce too many crisp if-then rules and is not an efficient way to represent the knowledge, especially in real-life data mining application. In this paper, we propose a novel associative classification method called FCBI, i.e., Fuzzy Classification Based on Implication. This method partitions the original data set into fuzzy table without discretization on continuous attributes, the rule generation is performed in the relational database system by using fuzzy implication table. The unique features of this method include its high training speed and simplicity in implementation. Experiment results show that the classification rules generated are meaningful and explainable.