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