Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection

In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR)...

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Autores principales:	Wei Wang, Xiaohui Hu, Yao Du
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Anomaly Detection Jarvis-Patrick Clustering Extended Shared Nearest Neighbor Outlier Detection Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/201ada37ca844889ab64e6b54d00f57c
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spelling	oai:doaj.org-article:201ada37ca844889ab64e6b54d00f57c2021-11-30T04:13:57ZAlgorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection1110-016810.1016/j.aej.2021.08.009https://doaj.org/article/201ada37ca844889ab64e6b54d00f57c2022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821005251https://doaj.org/toc/1110-0168In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR) of the algorithm, we use an extra outlier detection step combined with our proposed EJP to create a new anomaly detection method called LD-EJP. Using LD-EJP, the false alarm rate improved much (experiments show that the false alarm rate can reach 4.1% while the best JP clustering can reach is 7.4%). Then, we tested LD-EJP against two other anomaly detection methods using k-means and LGCCB, showing that our algorithm has a better detection rate and false alarm rate than these two clustering-based anomaly detection methods. In addition, the detection rate and false positives of the algorithm also have some room for improvement. In the labeling process, the proportion of anomaly clusters to normal clusters needs to be manually adjusted to find a better detection rate. In addition, the detection rate we chose can consume some of the detection rate gained in extended JP clustering to have the LD-EJP obtain a better FAR. Therefore, our future work contains finding or proposing another outlier detection algorithm with better performance than our LD-EJP method.Wei WangXiaohui HuYao DuElsevierarticleAnomaly DetectionJarvis-Patrick ClusteringExtended Shared Nearest NeighborOutlier DetectionEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 2106-2115 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anomaly Detection Jarvis-Patrick Clustering Extended Shared Nearest Neighbor Outlier Detection Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Anomaly Detection Jarvis-Patrick Clustering Extended Shared Nearest Neighbor Outlier Detection Engineering (General). Civil engineering (General) TA1-2040 Wei Wang Xiaohui Hu Yao Du Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
description	In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR) of the algorithm, we use an extra outlier detection step combined with our proposed EJP to create a new anomaly detection method called LD-EJP. Using LD-EJP, the false alarm rate improved much (experiments show that the false alarm rate can reach 4.1% while the best JP clustering can reach is 7.4%). Then, we tested LD-EJP against two other anomaly detection methods using k-means and LGCCB, showing that our algorithm has a better detection rate and false alarm rate than these two clustering-based anomaly detection methods. In addition, the detection rate and false positives of the algorithm also have some room for improvement. In the labeling process, the proportion of anomaly clusters to normal clusters needs to be manually adjusted to find a better detection rate. In addition, the detection rate we chose can consume some of the detection rate gained in extended JP clustering to have the LD-EJP obtain a better FAR. Therefore, our future work contains finding or proposing another outlier detection algorithm with better performance than our LD-EJP method.
format	article
author	Wei Wang Xiaohui Hu Yao Du
author_facet	Wei Wang Xiaohui Hu Yao Du
author_sort	Wei Wang
title	Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
title_short	Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
title_full	Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
title_fullStr	Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
title_full_unstemmed	Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection
title_sort	algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/201ada37ca844889ab64e6b54d00f57c
work_keys_str_mv	AT weiwang algorithmoptimizationandanomalydetectionsimulationbasedonextendedjarvispatrickclusteringandoutlierdetection AT xiaohuihu algorithmoptimizationandanomalydetectionsimulationbasedonextendedjarvispatrickclusteringandoutlierdetection AT yaodu algorithmoptimizationandanomalydetectionsimulationbasedonextendedjarvispatrickclusteringandoutlierdetection
_version_	1718406790761152512

Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection

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