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Evaluation of the Orion Outlier Detection Algorithm for Data Streams
Poster Title: Evaluation of the Orion Outlier Detection Algorithm for Data Streams
Submitted on 17 Feb 2019
Author(s): Nam Phung
Affiliations: University of Minnesota Duluth
This poster was presented at MinneWIC 2019
Poster Views: 396
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Poster Information
Abstract: Outlier detection in data streams has important applications in many big data applications. A data stream is a sequence of unbounded data points with transiency property in which older points are less significant than recent ones.

Data stream imposes a new challenge for outlier detection algorithms due to its nature in which the distribution and characteristics of data may change over time. There have been several outlier detection algorithms proposed in the past. One of them is Orion, a density-based outlier detection algorithm for data streams. This poster studies Orion and suggests optimization strategies to enhance its performance.
Summary: In this research study, we implemented in Java a density-based outlier detection algorithm called Orion, evaluated its performance and compared the results with several other outlier detection algorithms for data streams.References: [1] S. Sadik, L. Gruenwald and E. Leal, "In pursuit of outliers in multi-dimensional data streams," 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 512-521.doi: 10.1109/BigData.2016.7840642
[2] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
[3] Luan Tran, Liyue Fan, and Cyrus Shahabi. 2016. Distance-based outlier detection in data streams. Proceedings of the VLDB Endowment9, 12 (2016), 1089–1100.
[4] Distance-based outlier detection in data streams repository.
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