An Outlier Detection Method based on Fuzzy C-Means Clustering

被引：0
|
作者：
Li, Qiang [1 ]
Zhang, Jianpei [1 ]
Feng, Guangsheng [1 ]
机构：
[1] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China
来源：
ADVANCED DESIGN AND MANUFACTURE II | 2010年 / 419-420卷
关键词：
Clustering; Fuzzy c-means; Outlier; Information Theory; Entropy;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：165 / 168
页数：4
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