Paper Title:
An Outlier Detection Method Based on Fuzzy C-Means Clustering
  Abstract

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, J. P. Zhang, G. S. Feng, "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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