Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm

Liyue Chen; Tao Tao; Lizhong Zhang; Bing Lu; Zhongling Hang

doi:doi:10.11648/j.acis.20150305.12

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Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm

Liyue Chen, Tao Tao, Lizhong Zhang, Bing Lu, Zhongling Hang

Published in Automation, Control and Intelligent Systems (Volume 3, Issue 5)

Received: 1 September 2015 Accepted: 19 September 2015 Published: 29 September 2015

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Abstract

Nowadays data streams are more and more involved in the real industry. In this paper, the authors apply the data stream clustering to the electric power remote anomaly detection and propose a new data stream clustering algorithm based on density and grid (density-based data stream clustering algorithm, DBClustream). The double-frame analysis model is used in the proposed algorithm. In the online component, the authors optimize the initialization of the parameters for the K-means algorithm with a method based on density and grid and use the kernels to represent the micro clusters as the result of the online component. In the offline component, the time fading weight and the dynamic threshold to optimize the performance of the DENCLUE algorithm are proposed. To evaluate the performance of the proposed algorithm, both the evaluation of the anomaly detection and the evaluation of the data stream clustering are adopted. As the experiment result demonstrates, compared with the others algorithms, DBClustream can resolve the multi-density data stream and keep the high detection rate as well as the low false positive rate.

Published in	Automation, Control and Intelligent Systems (Volume 3, Issue 5)
DOI	10.11648/j.acis.20150305.12
Page(s)	71-75
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), 2015. Published by Science Publishing Group

Keywords

Anomaly Detection, Density Based, Clustering Algorithm, Data Stream

References

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[3]	WANG Jing, “Research of Online Intelligent Alarm,” M.S. thesis, School of Electrical Sys-tem and Automation, North China Electric Power University, Beijing, China, 2008.
[4]	Zhiwei SUN, Zheng ZHAO. “A Fast Clustering Algorithm Based on Grid and Density,” Electrical and Computer Engineering, pp.2276-2279, May 2005.
[5]	B. Borah, D. K. Bhattacharyya. “An Improved Sampling-Based DBSCAN for Large Spatial Databases,” Int. Conf. on Intelligent Sensing, pp. 92–96, 2004.
[6]	G. Wei, H. Wu. “LD-BSCA: A Local Density Based Spatial Clustering Algorithm,” in IEEE Symposium on Computational Intelligence and Data Mining. IEEE Computer Society, 1999, pp. 291–298.
[7]	Aggarwal C, Han J. “A Framework for Clustering Evolving Data Streams,” Proceedings of the 29th VLDB Conference, pp. 81-92, 2003.
[8]	F. CAO, M. ESTER. “Density-based clustering over an evolving data stream with noise,” Proceedings of the 2006 SIAM International Conference on Data Mining, pp.328-339, 2006.
[9]	M. Kumar, A. Sharma. “Mining of Data Stream Using DDen Stream Clustering Algorithm,” IEEE International Conference in MOOC, pp. 315-320, 2013, doi: 10.1109/MITE.2013.6756357.
[10]	Y. Chen, L. Tu. “Density-based clustering for real-time stream data,” Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 133–142, 2007.
[11]	Amineh Amini, Hadi Saboohi. “A Multi Density-based Clustering Algorithm for Data Stream with Noise,” IEEE on Data Mining Workshops, pp. 1105-1112, 2013, doi: 10.1109/ICDMW.2013.170.
[12]	ZHOU Ya-jian, XU Chen. “Unsupervised Anomaly Detection Method Based on Improved CURE Clustering Algorithm,” Journal on Communications, vol. 31, no. 7, pp. 18-23, 2010.
[13]	CUI Guan-xun, LI Liang. “Research on an Intrusion Detection System Based on the Im-proved Apriori Algorithm,” Computer Engineering & Science, vol. 33, no. 4, pp. 40-44, 2011.

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APA Style

Liyue Chen, Tao Tao, Lizhong Zhang, Bing Lu, Zhongling Hang. (2015). Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Automation, Control and Intelligent Systems, 3(5), 71-75. https://doi.org/10.11648/j.acis.20150305.12

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ACS Style

Liyue Chen; Tao Tao; Lizhong Zhang; Bing Lu; Zhongling Hang. Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Autom. Control Intell. Syst. 2015, 3(5), 71-75. doi: 10.11648/j.acis.20150305.12

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AMA Style

Liyue Chen, Tao Tao, Lizhong Zhang, Bing Lu, Zhongling Hang. Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Autom Control Intell Syst. 2015;3(5):71-75. doi: 10.11648/j.acis.20150305.12

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@article{10.11648/j.acis.20150305.12,
  author = {Liyue Chen and Tao Tao and Lizhong Zhang and Bing Lu and Zhongling Hang},
  title = {Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {5},
  pages = {71-75},
  doi = {10.11648/j.acis.20150305.12},
  url = {https://doi.org/10.11648/j.acis.20150305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150305.12},
  abstract = {Nowadays data streams are more and more involved in the real industry. In this paper, the authors apply the data stream clustering to the electric power remote anomaly detection and propose a new data stream clustering algorithm based on density and grid (density-based data stream clustering algorithm, DBClustream). The double-frame analysis model is used in the proposed algorithm. In the online component, the authors optimize the initialization of the parameters for the K-means algorithm with a method based on density and grid and use the kernels to represent the micro clusters as the result of the online component. In the offline component, the time fading weight and the dynamic threshold to optimize the performance of the DENCLUE algorithm are proposed. To evaluate the performance of the proposed algorithm, both the evaluation of the anomaly detection and the evaluation of the data stream clustering are adopted. As the experiment result demonstrates, compared with the others algorithms, DBClustream can resolve the multi-density data stream and keep the high detection rate as well as the low false positive rate.},
 year = {2015}
}

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TY  - JOUR
T1  - Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm
AU  - Liyue Chen
AU  - Tao Tao
AU  - Lizhong Zhang
AU  - Bing Lu
AU  - Zhongling Hang
Y1  - 2015/09/29
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150305.12
DO  - 10.11648/j.acis.20150305.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 71
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150305.12
AB  - Nowadays data streams are more and more involved in the real industry. In this paper, the authors apply the data stream clustering to the electric power remote anomaly detection and propose a new data stream clustering algorithm based on density and grid (density-based data stream clustering algorithm, DBClustream). The double-frame analysis model is used in the proposed algorithm. In the online component, the authors optimize the initialization of the parameters for the K-means algorithm with a method based on density and grid and use the kernels to represent the micro clusters as the result of the online component. In the offline component, the time fading weight and the dynamic threshold to optimize the performance of the DENCLUE algorithm are proposed. To evaluate the performance of the proposed algorithm, both the evaluation of the anomaly detection and the evaluation of the data stream clustering are adopted. As the experiment result demonstrates, compared with the others algorithms, DBClustream can resolve the multi-density data stream and keep the high detection rate as well as the low false positive rate.
VL  - 3
IS  - 5
ER  -

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Author Information

Liyue Chen

Electric power dispatching control center, State Grid Zhejiang Electric Power Company, Hangzhou, China
Tao Tao

Electric power dispatching control center, State Grid Zhejiang Electric Power Company, Hangzhou, China
Lizhong Zhang

Electric power dispatching control center, State Grid Zhejiang Electric Power Company, Hangzhou, China
Bing Lu

Electric power dispatching control center, State Grid Zhejiang Electric Power Company, Hangzhou, China
Zhongling Hang

Department of Automation, Shanghai Jiao Tong University, Shanghai, China

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Plain Text BibTeX RIS

APA Style

Liyue Chen, Tao Tao, Lizhong Zhang, Bing Lu, Zhongling Hang. (2015). Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Automation, Control and Intelligent Systems, 3(5), 71-75. https://doi.org/10.11648/j.acis.20150305.12

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ACS Style

Liyue Chen; Tao Tao; Lizhong Zhang; Bing Lu; Zhongling Hang. Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Autom. Control Intell. Syst. 2015, 3(5), 71-75. doi: 10.11648/j.acis.20150305.12

Copy | Download

AMA Style

Liyue Chen, Tao Tao, Lizhong Zhang, Bing Lu, Zhongling Hang. Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm. Autom Control Intell Syst. 2015;3(5):71-75. doi: 10.11648/j.acis.20150305.12

Copy | Download

@article{10.11648/j.acis.20150305.12,
  author = {Liyue Chen and Tao Tao and Lizhong Zhang and Bing Lu and Zhongling Hang},
  title = {Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {5},
  pages = {71-75},
  doi = {10.11648/j.acis.20150305.12},
  url = {https://doi.org/10.11648/j.acis.20150305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150305.12},
  abstract = {Nowadays data streams are more and more involved in the real industry. In this paper, the authors apply the data stream clustering to the electric power remote anomaly detection and propose a new data stream clustering algorithm based on density and grid (density-based data stream clustering algorithm, DBClustream). The double-frame analysis model is used in the proposed algorithm. In the online component, the authors optimize the initialization of the parameters for the K-means algorithm with a method based on density and grid and use the kernels to represent the micro clusters as the result of the online component. In the offline component, the time fading weight and the dynamic threshold to optimize the performance of the DENCLUE algorithm are proposed. To evaluate the performance of the proposed algorithm, both the evaluation of the anomaly detection and the evaluation of the data stream clustering are adopted. As the experiment result demonstrates, compared with the others algorithms, DBClustream can resolve the multi-density data stream and keep the high detection rate as well as the low false positive rate.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Electric Power Remote Monitor Anomaly Detection with a Density-Based Data Stream Clustering Algorithm
AU  - Liyue Chen
AU  - Tao Tao
AU  - Lizhong Zhang
AU  - Bing Lu
AU  - Zhongling Hang
Y1  - 2015/09/29
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150305.12
DO  - 10.11648/j.acis.20150305.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 71
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150305.12
AB  - Nowadays data streams are more and more involved in the real industry. In this paper, the authors apply the data stream clustering to the electric power remote anomaly detection and propose a new data stream clustering algorithm based on density and grid (density-based data stream clustering algorithm, DBClustream). The double-frame analysis model is used in the proposed algorithm. In the online component, the authors optimize the initialization of the parameters for the K-means algorithm with a method based on density and grid and use the kernels to represent the micro clusters as the result of the online component. In the offline component, the time fading weight and the dynamic threshold to optimize the performance of the DENCLUE algorithm are proposed. To evaluate the performance of the proposed algorithm, both the evaluation of the anomaly detection and the evaluation of the data stream clustering are adopted. As the experiment result demonstrates, compared with the others algorithms, DBClustream can resolve the multi-density data stream and keep the high detection rate as well as the low false positive rate.
VL  - 3
IS  - 5
ER  -

Copy | Download