An Outlier Detection Method Based on Fuzzy C-Means Clustering

Abstract:

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Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

Info:

Periodical:

Key Engineering Materials (Volumes 419-420)

Edited by:

Daizhong Su, Qingbin Zhang and Shifan Zhu

Pages:

165-168

DOI:

10.4028/www.scientific.net/KEM.419-420.165

Citation:

Q. Li et al., "An Outlier Detection Method Based on Fuzzy C-Means Clustering ", Key Engineering Materials, Vols. 419-420, pp. 165-168, 2010

Online since:

October 2009

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Price:

$35.00

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