A framework of cluster decision tree in data stream classification

摘要：

Recently, data streams classification with concept drifting has drawn increasing attention of scholars in data mining, due to the deficiencies of existing algorithms in accuracy and efficient. In this paper, we propose a framework for handling the problem mentioned above using cluster decision tree. We cluster those data which cannot be classified temporarily into n class, and generate new branches of the VFDT based on cluster result or replace original ones. Our empirical study shows that the proposed method has substantial advantages over traditional classifiers in prediction accuracy and efficiency.

关键词： data stream concept drifting cluster classification

作者: Lin Qian Liang-xi Qin

作者单位: School of Computer and Electronics Information, Guangxi University, Nanning, China Nanjing Nari Grou School of Computer and Electronics Information, Guangxi University, Nanning, China

会议类型: 国际会议

会议名称: 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics 第4届智能人机系统与控制论国际会议 IHMSC 2012

会议地点: 南昌

会议语种:英文

页码: 38-41

在线出版日期: 2012-08-26（万方平台首次上网日期，不代表论文的发表时间）