TY - JOUR
T1 - Tadilof
T2 - Time aware density-based incremental local outlier detection in data streams
AU - Huang, Jen Wei
AU - Zhong, Meng Xun
AU - Jaysawal, Bijay Prasad
N1 - Funding Information:
Funding: The work is funded by Ministry of Science and Technology, Taiwan (MOST 105-EPA-F-007-004).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/10/2
Y1 - 2020/10/2
N2 - 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means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data.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.
AB - 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means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data.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.
UR - http://www.scopus.com/inward/record.url?scp=85092717636&partnerID=8YFLogxK
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U2 - 10.3390/s20205829
DO - 10.3390/s20205829
M3 - Article
C2 - 33076325
AN - SCOPUS:85092717636
SN - 1424-8220
VL - 20
SP - 1
EP - 25
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 20
M1 - 5829
ER -