Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series

Hu Shaolin; Huang Xiaomin; Su Naiqian; Wang Shihua

doi:doi:10.11648/j.ijdst.20210703.12

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Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series

Hu Shaolin, Huang Xiaomin, Su Naiqian, Wang Shihua

Published in International Journal on Data Science and Technology (Volume 7, Issue 3)

Received: 1 August 2021 Accepted: 16 August 2021 Published: 27 August 2021

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Abstract

Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.

Published in	International Journal on Data Science and Technology (Volume 7, Issue 3)
DOI	10.11648/j.ijdst.20210703.12
Page(s)	54-61
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Data Clustering, Time Series, Change Detection

References

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[4]	Mao H B, Wu H S, Li Z X, et al. Research on similarity measurement methods for multivariate time series [J]. Control and Decision, 2011, 26 (4): 565-570.
[5]	Aghabozorgi S, Shirkhorshidi A S, Wah T Y. Time-series clustering – A decade review [J]. Information Systems, 2015, 53 (C): 16-38.
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[7]	Ke, Yi, Zhou. An improved morphological weighted dynamic similarity measurement algorithm for time series data [J]. International Journal of Intelligent Computing & Cybernetics, 2018.
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[9]	Balasubramaniyan R, Huellermeier E, Weskamp N, et al. Clustering of gene expression data using a local shape-based similarity measure [J]. Bioinformatics, 2005, 21 (7): 1069-1077.
[10]	Shumway R, Stoffer D. Time series analysis and its applications with R examples. New York: Springer, 2009.
[11]	Nguyen H. A New Similarity Measure for Intuitionistic Fuzzy Sets [C]// Asian Conference on Intelligent Information and Database Systems. Springer, Berlin, Heidelberg, 2016.
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[14]	Hanlon B, Forbes C. Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression [J]. Monash Econometrics and Business Stats Working Papers, 2002.
[15]	Izakian H, Pedrycz W, Jamal I. Fuzzy clustering of time series data using dynamic time warping distance [J]. Engineering Applications of Artificial Intelligence, 2015, 39 (mar.): 235-244.

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APA Style

Hu Shaolin, Huang Xiaomin, Su Naiqian, Wang Shihua. (2021). Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. International Journal on Data Science and Technology, 7(3), 54-61. https://doi.org/10.11648/j.ijdst.20210703.12

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ACS Style

Hu Shaolin; Huang Xiaomin; Su Naiqian; Wang Shihua. Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. Int. J. Data Sci. Technol. 2021, 7(3), 54-61. doi: 10.11648/j.ijdst.20210703.12

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AMA Style

Hu Shaolin, Huang Xiaomin, Su Naiqian, Wang Shihua. Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. Int J Data Sci Technol. 2021;7(3):54-61. doi: 10.11648/j.ijdst.20210703.12

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@article{10.11648/j.ijdst.20210703.12,
  author = {Hu Shaolin and Huang Xiaomin and Su Naiqian and Wang Shihua},
  title = {Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series},
  journal = {International Journal on Data Science and Technology},
  volume = {7},
  number = {3},
  pages = {54-61},
  doi = {10.11648/j.ijdst.20210703.12},
  url = {https://doi.org/10.11648/j.ijdst.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20210703.12},
  abstract = {Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.},
 year = {2021}
}

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TY  - JOUR
T1  - Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series
AU  - Hu Shaolin
AU  - Huang Xiaomin
AU  - Su Naiqian
AU  - Wang Shihua
Y1  - 2021/08/27
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdst.20210703.12
DO  - 10.11648/j.ijdst.20210703.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 54
EP  - 61
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20210703.12
AB  - Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.
VL  - 7
IS  - 3
ER  -

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Author Information

Hu Shaolin

Institute of Intelligent Perception and System Safety, Guangdong University of Petrochemical Technology, Maoming, China
Huang Xiaomin

School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China
Su Naiqian

Institute of Intelligent Perception and System Safety, Guangdong University of Petrochemical Technology, Maoming, China
Wang Shihua

Institute of Intelligent Perception and System Safety, Guangdong University of Petrochemical Technology, Maoming, China

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Plain Text BibTeX RIS

APA Style

Hu Shaolin, Huang Xiaomin, Su Naiqian, Wang Shihua. (2021). Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. International Journal on Data Science and Technology, 7(3), 54-61. https://doi.org/10.11648/j.ijdst.20210703.12

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ACS Style

Hu Shaolin; Huang Xiaomin; Su Naiqian; Wang Shihua. Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. Int. J. Data Sci. Technol. 2021, 7(3), 54-61. doi: 10.11648/j.ijdst.20210703.12

Copy | Download

AMA Style

Hu Shaolin, Huang Xiaomin, Su Naiqian, Wang Shihua. Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series. Int J Data Sci Technol. 2021;7(3):54-61. doi: 10.11648/j.ijdst.20210703.12

Copy | Download

@article{10.11648/j.ijdst.20210703.12,
  author = {Hu Shaolin and Huang Xiaomin and Su Naiqian and Wang Shihua},
  title = {Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series},
  journal = {International Journal on Data Science and Technology},
  volume = {7},
  number = {3},
  pages = {54-61},
  doi = {10.11648/j.ijdst.20210703.12},
  url = {https://doi.org/10.11648/j.ijdst.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20210703.12},
  abstract = {Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series
AU  - Hu Shaolin
AU  - Huang Xiaomin
AU  - Su Naiqian
AU  - Wang Shihua
Y1  - 2021/08/27
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdst.20210703.12
DO  - 10.11648/j.ijdst.20210703.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 54
EP  - 61
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20210703.12
AB  - Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.
VL  - 7
IS  - 3
ER  -

Copy | Download