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噪声数据流的分类方法 被引量：2

Classification method of noise data stream
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摘要 数据流中噪声数据的处理是当前数据流分类挖掘中重要的研究分支,近些年来得到了广泛的关注.本文提出了一种称为FDBCA的数据流分类算法.它使用基于密度的带有噪声的空间聚类(DBSCAN)的改进算法Fast-DB-SCAN(FDBSCAN)处理噪声数据,并利用错误率方差(MSE)来检测概念漂移.同已有的数据流分类算法相比,实验结果表明了FDBCA算法可以提高噪声数据流的分类精度. Processing noise data from data stream is one of the most important fields in data stream classification.In this paper,a new classification algorithm called FDBCA is proposed,which deals with noise data by Fast-DBSCAN that improved density based spatial clustering of applications with noise algorithm,and use mean square error to detect concept drifting.The experimental results show that FDBCA can improve classification accuracy on classifying noise data stream compared with existed data stream classification algorithm.
作者 陈丙杰 王晓晔 常飞
机构地区 天津理工大学计算机与通信工程学院
出处 《天津理工大学学报》 2011年第3期37-41,共5页 Journal of Tianjin University of Technology
关键词 数据流 概念漂移 FDBSCAN 分类算法 data stream concept drifting FDBSCAN classification algorithm
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献6

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二级参考文献28

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共引文献39

同被引文献20

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天津理工大学学报
2011年 第3期

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