关键词: 数据挖掘, 关联分类, 类关联规则, 规则兴趣度

Abstract: Associative classification has high classification accuracy and strong flexibility.However，it still suffers from overfitting since the classification is based on high confidence rules.This paper has proposed a new Associative Classification based on Interestingness of Rules（ACIR）.ACIR extends TD-FP-growth to mine interesting rules with min-support and min-confidence，prunes rules and selects a small rule set to build the classifier.ACIR evaluates rules based on interestingness of rules which includes predictive accuracy and interestingness of the rule items.Experimental results show that ACIR has better classification accuracy in comparison with See5，CBA and CMAR and are highly comprehensible and scalable.

Key words: data mining, associative classification, class association rules, interestingness of rules