LDBOD: A novel local distribution based outlier detector

Authors:
Yong Zhang;Su Yang;Yuanyuan Wang
Affiliations:
Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China;Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China;Department of Electronic Engineering, Fudan University, Shanghai 200433, PR China
Venue:
Pattern Recognition Letters
Year:
2008

Citing 13
Cited 1

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Enhancing effectiveness of density-based outlier mining scheme with density-similarity-neighbor-based outlier factor

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Abstract

As an important research direction in KDD field, outlier detection has been drawing much attention from different communities. In this paper, two novel algorithms LDBOD and LDBOD+ for outlier detection are proposed. Similar to LOF, they also aim to find local outliers. However, LDBOD/LDBOD+ detects local outliers from the viewpoint of local distribution, which is characterized through three proposed measurements, local-average-distance, local-density, and local-asymmetry-degree. Several experiments were conducted to demonstrate the advantages of LDBOD/LDBOD+ compared with LOF.