NDLI: Outlier Detection with Enhanced Angle-based Outlier Factor in High-dimensional Data Stream

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Outlier Detection with Enhanced Angle-based Outlier Factor in High-dimensional Data Stream

Content Provider	Semantic Scholar
Author	Shou, Zhaoyu Tian, Hao Li, Simin Zou, Fengbo
Copyright Year	2018
Abstract	Outlier detection over data stream is an increasingly important research in many fields. Traditional methods are no longer applicable. In this paper, a novel outlier detection algorithm with enhanced angle-based outlier factor in high-dimensional data stream (EAOF-OD) is proposed. EAOF-OD aims at improving the performance of outlier detection and reducing the consumption of memory. To measure the deviation degree of potential outliers accurately in sophisticated high-dimensional datasets, an enhanced angle-based outlier factor is introduced. To ensure the high detection rate, the proposed scheme first locates the cluster centers and divides the dataset into several clusters, and then outlier detection is carried out within each cluster. Furthermore, an efficient model based on sliding window and multiple validations is presented in order to decrease the false alarm rate, which divides data stream into uniform-sized blocks and declares a point far away from its cluster as candidate outlier. With new block joining in and historical block moving out, the sliding window reserves the most valuable information including candidate outliers which need multiple validations. Comparison experiments with existing approaches on synthetic and real datasets demonstrate that EAOF-OD outperforms some existing approaches in terms of outlier detection rate and false alarm rate.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijicic.org/ijicic-140506.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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