A new method for outlier detection using center of gravity  

Oleh:

Ha, Jihyun ; Seok, Seulgi ; Lee, Jong-Seok

Jenis: Article from Proceeding
Dalam koleksi: The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3-6 December 2013 Cebu, Philippines, page 1-9.
Topik: Outlier detection; Noise removal; Instability factor; Nearest neighbors
Fulltext: 1017.pdf (516.29KB)

Isi artikel

Since outlier detection is applicable to various fields such as financial, telecommunication, medical, and commercial industry, its importance is radically increasing. Receiving such a great attention leads to developing many detection methods, and most of those belong to one of two categories, namely, distance-based and density-based approach, where each has its intrinsic weakness. The former hardly detects local outliers, while the latter has low density patterns problem. To overcome the weaknesses, we propose a new detection method that introduces the instability factor of a data point by utilizing the concept of center of gravity. The proposed method can be flexibly used for both local and global detection of outliers by controlling its parameter. Numerical experiments based on artificial datasets show the effectiveness of the proposed method.

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