Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

Xiaoji Wan, Hailin Li, Liping Zhang, Yenchun Jim Wu*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.

原文英語
文章編號9915315
期刊Wireless Communications and Mobile Computing
2021
DOIs
出版狀態已發佈 - 2021

ASJC Scopus subject areas

  • 資訊系統
  • 電腦網路與通信
  • 電氣與電子工程

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