关键词: 数据挖掘, 关联分类, 两次学习, 规则冲突

Abstract:

Associative classification usually generates numerous rules, resulting in rule conflicts in stage of classification. To address this problem, a double learning method based on the improved associative classification is proposed. The improved associative classification reduces the number of rules significantly by discovering the frequent and mutual associated itemsets. All training conflict instances in training set are separated by applying the first level rules generated by the improved associative classification. Then, the second level rule set is induced by applying the improved associative classification on the conflict instances. When classifying a new instance, the first level rule set is applied. If the rules are not consistent with the instance, the second rules set is used to classify this instance. The experimental results show that the double learning method based on the improved associative classification can improve the classification accuracy effectively.

Key words: data mining;associative classification;double learning;conflict rules