Similarity dynamical clustering algorithm based on multidimensional shape features for time series

WANG Ling; MENG Jian-yao; XU Pei-pei; PENG Kai-xiang

doi:10.13374/j.issn2095-9389.2017.07.019

Volume 39 Issue 7

Jul. 2017

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WANG Ling, MENG Jian-yao, XU Pei-pei, PENG Kai-xiang. Similarity dynamical clustering algorithm based on multidimensional shape features for time series[J]. Chinese Journal of Engineering, 2017, 39(7): 1114-1122. doi: 10.13374/j.issn2095-9389.2017.07.019

Citation:

WANG Ling, MENG Jian-yao, XU Pei-pei, PENG Kai-xiang. Similarity dynamical clustering algorithm based on multidimensional shape features for time series[J]. Chinese Journal of Engineering, 2017, 39(7): 1114-1122. doi: 10.13374/j.issn2095-9389.2017.07.019

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Similarity dynamical clustering algorithm based on multidimensional shape features for time series

doi: 10.13374/j.issn2095-9389.2017.07.019

WANG Ling^{1,2
,
,},
MENG Jian-yao^1,2,
XU Pei-pei^1,2,
PENG Kai-xiang^1,2

1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2) Key Laboratory of Knowledge Automationfor Industrial Processes(Ministry of Education), University of Science and Technology Beijing, Beijing 100083, China

Received Date: 2017-01-03

Abstract

Abstract

Traditional data mining methods are difficult to deal with the high dimensionality and dynamics characteristic of the time series. Therefore, in this study, a similarity dynamical clustering algorithm based on multidimensional shape features for time series (SDCTS) was proposed. First, the feature points of multidimensional time series are extracted to realize dimensionality reduction. Second, a new similarity measure criterion is defined with the shape features (slope, length, and amplitude) of the obtained multidimensional time series, and thus a dynamical clustering algorithm of multidimensional time series is proposed without predefining clustering numbers. The experimental results demonstrate that the SDCTS algorithm improves the clustering accuracy for time series compared with other algorithms.
- similarity measure,
- clustering,
- time series,
- shape features,
- dimensionality reduction

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References(5)

References

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