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数据挖掘中分类算法的可扩展性研究
引用本文:乔向杰,陈功平.数据挖掘中分类算法的可扩展性研究[J].信阳师范学院学报(自然科学版),2006,19(2):239-242.
作者姓名:乔向杰  陈功平
作者单位:1. 北京科技大学,信息工程学院,北京,100083;信阳师范学院,计算机科学系,河南,信阳,464000
2. 信阳师范学院,计算机科学系,河南,信阳,464000
摘    要:分类是数据挖掘中最重要的技术之一,而且应用领域非常广泛,但面对新出现的海量数据,目前已有的许多分类算法不具备良好的伸缩性,不能从巨大的数据集中快速而准确地发现有用的知识.针对这一问题,本文对分类算法的可扩展性方法进行了深入的研究,并对各种方法进行了分析和对比,从而便于研究和开发者对已有的算法进行改进和扩展,以适应数据挖掘技术的不断发展.

关 键 词:分类  大数据集  可扩展性
文章编号:1003-0972(2006)02-0239-04
收稿时间:2005-01-16
修稿时间:2005-09-20

Studies on Scalability of Classification Algorithm in Data mining
QIAO Xiang-jie,CHEN Gong-ping.Studies on Scalability of Classification Algorithm in Data mining[J].Journal of Xinyang Teachers College(Natural Science Edition),2006,19(2):239-242.
Authors:QIAO Xiang-jie  CHEN Gong-ping
Institution:1. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. Department of Computer Science, Xinyang Normal University, Xinyang 464000, China
Abstract:Classification is one of the most important techniques in data mining, it has been largely applied in many fields.However,as the appearance of large amount of data,the existing classification algorithms do not scale well,they cannot mine useful knowledge from very large datasets quickly and accurately.This paper discusses scalability,gives an analysis and comparison of these algorithms.So that researchers and practitioners can scale up their existing algorithms to catch up with the development of data mining technology.
Keywords:classification  large dataset  scalability
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