Novel algorithm for clustering heterogeneous data stream based on dissimilarity matrix

doi:10.3778/j.issn.1002-8331.2008.25.045

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (25): 149-151.DOI: 10.3778/j.issn.1002-8331.2008.25.045

• 数据库、信号与信息处理 • Previous Articles Next Articles

Novel algorithm for clustering heterogeneous data stream based on dissimilarity matrix

WAN Ren-xia,CHEN Jing-chao,WANG Li-xin

College of Information Science and Technology，Donghua University，Shanghai 201620，China

Received:2007-11-01 Revised:2008-01-21 Online:2008-09-01 Published:2008-09-01
Contact: WAN Ren-xia

基于相异度矩阵的混合属性数据流聚类算法

万仁霞,王立新,刘振文

东华大学信息科学与技术学院，上海 201620

通讯作者: 万仁霞

Abstract

Abstract: Data stream clustering is an important issue in data stream mining.In this paper，a novel algorithm is presented for clustering data stream with heterogeneous attributes.It adopts dissimilarity instead of the common clustering distance，and an equivalent dissimilarity matrix is used in the clustering process.Then the empirical evidence of this algorithm’s superiority over CluStream and HCluStream algorithms on the real data sets is given.

Key words: data stream, dissimilarity, cluster, heterogeneous attributes

摘要： 数据流的聚类是数据流挖掘的一个重要问题。提出一种针对混合属性的数据流聚类算法，它采用相异度来代替普通的聚类距离，并将等价相异度矩阵引入聚类过程。基于真实数据集的实验表明该算法比基地同类算法具有更好的聚类性能。

关键词: 数据流, 相异度, 聚类, 混合属性

WAN Ren-xia,CHEN Jing-chao,WANG Li-xin. Novel algorithm for clustering heterogeneous data stream based on dissimilarity matrix[J]. Computer Engineering and Applications, 2008, 44(25): 149-151.

万仁霞,王立新,刘振文. 基于相异度矩阵的混合属性数据流聚类算法[J]. 计算机工程与应用, 2008, 44(25): 149-151.

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Novel algorithm for clustering heterogeneous data stream based on dissimilarity matrix

基于相异度矩阵的混合属性数据流聚类算法

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