Tadilof: Time aware density-based incremental local outlier detection in data streams

Jen Wei Huang, Meng Xun Zhong, Bijay Prasad Jaysawal

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)

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means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data.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

原文English
文章編號5829
頁(從 - 到)1-25
頁數25
期刊Sensors (Switzerland)
20
發行號20
DOIs
出版狀態Published - 2020 10月 2

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 資訊系統
  • 儀器
  • 原子與分子物理與光學
  • 電氣與電子工程
  • 生物化學

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