标题: Mining decision rules on data streams in the presence of concept drifts
作者: Tsai, Cheng-Jung
Lee, Chien-I.
Yang, Wei-Pang
资讯工程学系
Department of Computer Science
关键字: Data mining;Classification;Decision tree;Data stream;Concept drift
公开日期: 1-三月-2009
摘要: In a database, the concept of all example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using the old dataset is not suitable for predicting a new dataset. Therefore, the problem of concept drift has attracted a lot of attention in recent years. Although many algorithms have been proposed to solve this problem, they have not been able to provide users with a satisfactory solution to concept drift. That is, the current research about concept drift focuses only on updating the classification model. However, real life decision makers might be very interested in the rules of concept drift. For example, doctors desire to know the root causes behind variation in the causes and development of disease. In this paper, we propose a concept drift rule mining tree, called CDR-Tree, to accurately discover the underlying rule governing concept drift. The main contributions of this paper are: (a) we address the problem of mining concept-drifting rules which has not been considered in previously developed classification schemes; (b) we develop a method that call accurately mine rules governing concept drift: (c) we develop a method that should classification models be required, call efficiently and accurately generate such models via a simple extraction procedure rather than constructing them anew; and (d) we propose two strategies to reduce the complexity of concept-drifting, rules mined by our CDR-Tree. (C) 2007 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2007.11.034
http://hdl.handle.net/11536/7537
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2007.11.034
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 36
Issue: 2
起始页: 1164
结束页: 1178
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