Adaptive associative classification with emerging frequent patterns  

作　　者：Wang Xiaofeng Zhang Dapeng Shi Zhongzhi 

机构地区：[1]The Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,P.R.China [2]Graduate University of Chinese Academy of Sciences,Beijing 100049,P.R.China [3]Institute of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,P.R.China

出　　处：《High Technology Letters》2012年第1期38-44,共7页高技术通讯（英文版）

基　　金：Supported by the National High Technology Research and Development Program of China （No. 2007AA01Z132）; the National Natural Science Foundation of China （No.60775035, 60933004, 60970088, 60903141）; the National Basic Research Priorities Programme （No. 2007CB311004）; the National Science and Technology Support Plan （No.2006BAC08B06）.

摘　　要：In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

关 键 词：associative classification RULE frequent pattern mining emerging frequent pattern supportvector machine （SVM） 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TQ013.1[自动化与计算机技术—计算机科学与技术]