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Fast Memory Efficient Local Outlier Detection in Data Streams

机译:数据流中快速的内存有效本地异常检测

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Outlier detection is an important task in data mining, with applications ranging from intrusion detection to human gait analysis. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the well-known Local Outlier Factor (LOF) algorithm has an incremental version, it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF_F), both have an accuracy close to Incremental LOF but within a fixed memory bound. Our experimental results show that both proposed approaches have better memory and time complexity than Incremental LOF while having comparable accuracy. In addition, we show that MiLOF_F is robust to changes in the number of data points, the number of underlying clusters and the number of dimensions in the data stream. These results show that MiLOF/MiLOF_F are well suited to application environments with limited memory (e.g., wireless sensor networks), and can be applied to high volume data streams.
机译:离群检测是数据挖掘中的重要任务,其应用范围从入侵检测到步态分析。随着对高速数据流进行分析的需求不断增长,离群值检测的任务变得更加具有挑战性,因为传统的离群值检测技术不再能够假设所有数据都可以存储用于处理。尽管众所周知的本地离群因子(LOF)算法具有增量版本,但它假定无限制内存来保留所有先前的数据点。在本文中,我们提出了一种用于数据流的内存高效增量式局部离群值(MiLOF)检测算法,以及一种更灵活的版本(MiLOF_F),两者的精度均接近增量LOF,但在固定的内存范围内。我们的实验结果表明,两种建议的方法都具有比增量LOF更好的内存和时间复杂性,同时具有相当的精度。此外,我们证明了MiLOF_F对于数据点数量,基础簇的数量以及数据流中维数的变化具有鲁棒性。这些结果表明,MiLOF / MiLOF_F非常适合内存有限的应用环境(例如,无线传感器网络),并且可以应用于大量数据流。

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