Building a more accurate classifier based on strong frequent patterns

The classification problem in data mining is to discover models from training data for classifying unknown instances. Associative classification builds the classifier rules using association rules and it is more accurate compared to previous methods. In this paper, a new method named CSFP that builds a classifier from strong frequent patterns without the need to generate association rules is presented. We address the rare item problem by using a partitioning method. Rules generated are stored using a compact data structure named CP-Tree and a series of pruning methods are employed to discard weak frequent patterns. Experimental results show that our classifier is more accurate than previous associative classification methods as well as other state-of-the-art non-associative classifiers.