Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (1): 137-139.DOI: 10.3778/j.issn.1002-8331.2010.01.042

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

Efficient clustering algorithm for multivariate time series

ZHOU Da-zhuo1,2,JIANG Wen-bo2,LI Min-qiang1   

  1. 1.School of Management,Tianjin University,Tianjin 300072,China
    2.Computer Center,Hebei University of Economics and Trade,Shijiazhuang 050061,China
  • Received:2008-07-24 Revised:2008-10-15 Online:2010-01-01 Published:2010-01-01
  • Contact: ZHOU Da-zhuo



  1. 1.天津大学 管理学院,天津 300072
    2.河北经贸大学 计算机中心,石家庄 050061
  • 通讯作者: 周大镯

Abstract: Time series clustering is an important issue in data mining research.Most of the existing algorithms adopt K-means method to cluster low dimension data,which are not suitable to address the problem of clustering high dimensional Multivariate Time Series(MTS) data.This paper proposes an efficient clustering algorithm for Multivariate Time Series—PCA-CLUSTER.The algorithm applies principal component analysis to reduce the dimension of MTS,and subsequently chooses the principal component series of MTS to cluster by a K-nearest neighbor algorithm.Theoretic analysis and experimental results show that PCA-CLUSTER is effective and efficient.

Key words: multivariate time series, clustering analysis, principal component analysis

摘要: 时间序列聚类分析是数据挖掘研究的一个重要内容。已有的聚类算法大多采用k均值对低维数据进行聚类,不能对高维多变量时间序列(MTS)数据进行有效聚类。提出一种高效的多变量时间序列聚类算法PCA-CLUSTER,首先利用主成分分析对MTS数据降维;选取MTS数据的主成分序列进行K近邻聚类分析。理论分析和实验结果表明算法可以有效解决MTS数据聚类问题。

关键词: 多变量时间序列, 聚类分析, 主成分分析

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