An Efficient Distance and Density Based Outlier Detection Approach
2012 ◽
Vol 155-156
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pp. 342-347
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In order to solve the density based outlier detection problem with low accuracy and high computation, a variance of distance and density (VDD) measure is proposed in this paper. And the k-means clustering and score based VDD (KSVDD) approach proposed can efficiently detect outliers with high performance. For illustration, two real-world datasets are utilized to show the feasibility of the approach. Empirical results show that KSVDD has a good detection precision.
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2020 ◽
Vol 34
(04)
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pp. 6837-6844
Keyword(s):
An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering
2016 ◽
Vol 2016
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pp. 1-14
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