TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outli...
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Autor*in:	Jen-Wei Huang [verfasserIn] Meng-Xun Zhong [verfasserIn] Bijay Prasad Jaysawal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	outlier detection local outlier factor data streams air quality monitoring

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 20(2020), 20, p 5829
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:20, p 5829

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s20205829

Katalog-ID:	DOAJ084760621

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520			\|a Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.
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spelling	10.3390/s20205829 doi (DE-627)DOAJ084760621 (DE-599)DOAJb892ec3c6a544686b96f9bf734d2f2e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jen-Wei Huang verfasserin aut TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system. outlier detection local outlier factor data streams air quality monitoring Chemical technology Meng-Xun Zhong verfasserin aut Bijay Prasad Jaysawal verfasserin aut In Sensors MDPI AG, 2003 20(2020), 20, p 5829 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:20, p 5829 https://doi.org/10.3390/s20205829 kostenfrei https://doaj.org/article/b892ec3c6a544686b96f9bf734d2f2e9 kostenfrei https://www.mdpi.com/1424-8220/20/20/5829 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 20, p 5829
allfields_unstemmed	10.3390/s20205829 doi (DE-627)DOAJ084760621 (DE-599)DOAJb892ec3c6a544686b96f9bf734d2f2e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jen-Wei Huang verfasserin aut TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system. outlier detection local outlier factor data streams air quality monitoring Chemical technology Meng-Xun Zhong verfasserin aut Bijay Prasad Jaysawal verfasserin aut In Sensors MDPI AG, 2003 20(2020), 20, p 5829 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:20, p 5829 https://doi.org/10.3390/s20205829 kostenfrei https://doaj.org/article/b892ec3c6a544686b96f9bf734d2f2e9 kostenfrei https://www.mdpi.com/1424-8220/20/20/5829 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 20, p 5829
allfieldsGer	10.3390/s20205829 doi (DE-627)DOAJ084760621 (DE-599)DOAJb892ec3c6a544686b96f9bf734d2f2e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jen-Wei Huang verfasserin aut TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system. outlier detection local outlier factor data streams air quality monitoring Chemical technology Meng-Xun Zhong verfasserin aut Bijay Prasad Jaysawal verfasserin aut In Sensors MDPI AG, 2003 20(2020), 20, p 5829 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:20, p 5829 https://doi.org/10.3390/s20205829 kostenfrei https://doaj.org/article/b892ec3c6a544686b96f9bf734d2f2e9 kostenfrei https://www.mdpi.com/1424-8220/20/20/5829 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 20, p 5829
allfieldsSound	10.3390/s20205829 doi (DE-627)DOAJ084760621 (DE-599)DOAJb892ec3c6a544686b96f9bf734d2f2e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jen-Wei Huang verfasserin aut TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system. outlier detection local outlier factor data streams air quality monitoring Chemical technology Meng-Xun Zhong verfasserin aut Bijay Prasad Jaysawal verfasserin aut In Sensors MDPI AG, 2003 20(2020), 20, p 5829 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:20, p 5829 https://doi.org/10.3390/s20205829 kostenfrei https://doaj.org/article/b892ec3c6a544686b96f9bf734d2f2e9 kostenfrei https://www.mdpi.com/1424-8220/20/20/5829 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 20, p 5829
language	English
source	In Sensors 20(2020), 20, p 5829 volume:20 year:2020 number:20, p 5829
sourceStr	In Sensors 20(2020), 20, p 5829 volume:20 year:2020 number:20, p 5829
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	outlier detection local outlier factor data streams air quality monitoring Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Jen-Wei Huang @@aut@@ Meng-Xun Zhong @@aut@@ Bijay Prasad Jaysawal @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Jen-Wei Huang
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topic	misc TP1-1185 misc outlier detection misc local outlier factor misc data streams misc air quality monitoring misc Chemical technology
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title	TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
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title_auth	TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
abstract	Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.
abstractGer	Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.
abstract_unstemmed	Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.
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title_short	TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
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