The Compact Fuzzy Association Rules For Data Classification

Abstract

Data classification mining is a method to find data generalization in a form of rules then used these rules to predict some unknown value in the future data. But in actual applications, the rules may be of low accuracy and the number of rules may be so overwhelmed that users could not efficiently apply them. Therefore, this research proposes the development of data classification algorithm with compact fuzzy association rules to optimize accuracy and interpretability of the model. To evaluate the performance of the proposed method, this research will compare accuracy of the classification model and the number of rules against 9 different data classification algorithms. The results showed that our CCFAR algorithm is comparable in terms of accuracy. When considering both accuracy and size of model, our algorithm is the best one.