Detecting pattern-based outliers
Pattern Recognition LettersPublished 8 August 2003Open access
Tianming Hu, Sam Yuan Sung
Citations58
SJR quartileQ4
SJR score0.11
SNIP0.06
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TL;DR
This work proposes two techniques: one to identify the two patterns called low density regularity and the other to detect the corresponding outliers.
Abstract
Outlier detection targets those exceptional data that deviate from the general pattern. Besides high density clustering, there is another pattern called low density regularity. Thus, there are two types of outliers w.r.t. them. We propose two techniques: one to identify the two patterns and the other to detect the corresponding outliers.
Keywords
Computer Science
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