Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35484
Title: A model-based multivariate time series clustering algorithm
Authors: Zhou, PY
Chan, KCC 
Keywords: Model-based clustering algorithm
Multivariate time series
Time series clustering
Issue Date: 2014
Publisher: Springer
Source: In WC Peng, H Wang, J Bailey, VS Tseng, TB Ho, ZH Zhou & ALP Chen (Eds.), Trends and applications in knowledge discovery and data mining : PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA,Tainan, Taiwan, May 13-16, 2014 : revised selected papers, p. 805-817. Cham : Springer, 2014 How to cite?
Series/Report no.: Lecture notes in computer science, 2014, v. 8548
Abstract: Given a set of multivariate time series, the problem of clustering such data is concerned with the discovering of inherent groupings of the data according to how similar or dissimilar the time series are to each other. Existing time series clustering algorithms can divide into three types, raw-based, feature-based and model-based. In this paper, a model-based multivariate time series clustering algorithm is proposed and its tasks in several steps: (i)data transformation, (ii)discovering time series temporal patterns using confidence value to represent the relationship between different variables, (iii) clustering of multi-variate time series based on the degree of patterns discovering in (ii). For evaluate performance of proposed algorithm, the proposed algorithm is tested with both synthetic data and real data. The result shows that it can be promising algorithm for multivariate time series clustering.
URI: http://hdl.handle.net/10397/35484
ISBN: 9783319131856 (print)
9783319131863 (electronic bk.)
DOI: 10.1007/978-3-319-13186-3_72
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