Proceedings Article10.1145/2790755.2790775
Continuous Angle-based Outlier Detection on High-dimensional Data Streams
Hao Ye,Hiroyuki Kitagawa,Jun Xiao +2 more
- 13 Jul 2015
- pp 162-167
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TL;DR: This paper proposes several incremental angle-based outlier detection approaches over data streams based on ABOD and its variants that provide visible speed-up without loss of accuracy.
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Abstract: Outlier detection over data streams is an increasingly important task in data mining. Traditional distance-based data stream outlier detection is unsuitable for high-dimensional data sets, since the discrimination of distances between different data points becomes rather poor in high dimensional space. ABOD (Angle-based Outlier Detection) is an effective approach to detecting outliers in high-dimensional space. In this paper, the problem of continuous ABOD over data streams is studied. Generally, only a few data objects may change their states during two consecutive timestamps. Therefore, we propose several incremental angle-based outlier detection approaches over data streams based on ABOD and its variants that provide visible speed-up without loss of accuracy. Firstly, the basic ideas of these incremental algorithms are introduced. Then, we explain the time complexity of them. Finally, we use synthetic data streams to prove their efficiency.
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A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations
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TL;DR: Different techniques of outlier detection in the data streams are reviewed and different approaches based on these techniques are described in order to establish a comparative study based on different criterion.
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TL;DR: This chapter provides an overview of the outlier detection problem and brings out various research issues connected with this problem.
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References
LOF: identifying density-based local outliers
Markus M. Breunig,Hans-Peter Kriegel,Raymond T. Ng,Jörg Sander +3 more
- 16 May 2000
TL;DR: This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
7.3K
•Book
Identification of outliers
Douglas M. Hawkins
- 01 Jan 1980
TL;DR: A computer normalizes the one or more sets of historical data points and creates a first visual representation corresponding to the first set of the oneor more sets and the second set of additional points.
2.8K
Distance-based outliers: algorithms and applications
Edwin M. Knorr,Raymond T. Ng,Vladimir Tucakov +2 more
- 01 Feb 2000
TL;DR: Outlier detection can be done efficiently for large datasets, and for k-dimensional datasets with large values of k, and it is shown that outlier detection is a meaningful and important knowledge discovery task.
1.3K
Outlier detection for high dimensional data
Charu C. Aggarwal,Philip S. Yu +1 more
- 01 May 2001
TL;DR: New techniques for outlier detection which find the outliers by studying the behavior of projections from the data set are discussed.
1.2K
Angle-based outlier detection in high-dimensional data
Hans-Peter Kriegel,Matthias Schubert,Arthur Zimek +2 more
- 24 Aug 2008
TL;DR: This paper proposes a novel approach named ABOD (Angle-Based Outlier Detection) and some variants assessing the variance in the angles between the difference vectors of a point to the other points and shows ABOD to perform especially well on high-dimensional data.