TY - GEN
T1 - A model-based multivariate time series clustering algorithm
AU - Zhou, Pei Yuan
AU - Chan, Chun Chung
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Model-based clustering algorithm
KW - Multivariate time series
KW - Time series clustering
UR - http://www.scopus.com/inward/record.url?scp=84915762815&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13186-3_72
DO - 10.1007/978-3-319-13186-3_72
M3 - Conference article published in proceeding or book
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 805
EP - 817
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
PB - Springer Verlag
T2 - International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
Y2 - 13 May 2014 through 16 May 2014
ER -