2008 LocalPeculiarityFactorandItsApp

(Yang et al., 2008) ⇒ Jian Yang, Ning Zhong, Yiyu Yao, and Jue Wang. (2008). “Local Peculiarity Factor and Its Application in Outlier Detection.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401983

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Abstract

Peculiarity oriented mining (POM), aiming to discover peculiarity rules hidden in a dataset, is a new data mining method. In the past few years, many results and applications on POM have been reported. However, there is still a lack of theoretical analysis. In this paper, we prove that the peculiarity factor (PF), one of the most important concepts in POM, can accurately characterize the peculiarity of data with respect to the probability density functionof a normal distribution, but is unsuitable for more general distributions. Thus, we propose the concept of local peculiarity factor (LPF). It is proved that the LPF has the same ability as the PF for a normal distribution and is the so-called µ-sensitive peculiarity description for general distributions. To demonstrate the effectiveness of the LPF, we apply it to outlier detection problems and give a new outlier detection algorithm called LPF-Outlier. Experimental results show that LPF-Outlier is an effective outlier detection algorithm.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 LocalPeculiarityFactorandItsApp	Ning Zhong Jian Yang Yiyu Yao Jue Wang			Local Peculiarity Factor and Its Application in Outlier Detection				10.1145/1401890.1401983