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

Sensors

Meng-Xun Zhong¹  BijayPrasad Jaysawal¹  Jen-Wei Huang¹ 

[1] Department of Electrical Engineering, National Cheng Kung University, Tainan City 701, Taiwan;

关键词: outlier detection;    local outlier factor;    data streams;    air quality monitoring;   

DOI  :  10.3390/s20205829

来源: DOAJ

【 摘 要 】

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means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data.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.

【 授权许可】

Unknown