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http://dx.doi.org/10.6109/jkiice.2011.15.8.1659

Initialization of Fuzzy C-Means Using Kernel Density Estimation  

Heo, Gyeong-Yong (동의대학교 영상미디어 센터)
Kim, Kwang-Baek (신라대학교 컴퓨터공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.15, no.8, 2011 , pp. 1659-1664 More about this Journal
Abstract
Fuzzy C-Means (FCM) is one of the most widely used clustering algorithms and has been used in many applications successfully. However, FCM has some shortcomings and initial prototype selection is one of them. As FCM is only guaranteed to converge on a local optimum, different initial prototype results in different clustering. Therefore, much care should be given to the selection of initial prototype. In this paper, a new initialization method for FCM using kernel density estimation (KDE) is proposed to resolve the initialization problem. KDE can be used to estimate non-parametric data distribution and is useful in estimating local density. After KDE, in the proposed method, one initial point is placed at the most dense region and the density of that region is reduced. By iterating the process, initial prototype can be obtained. The initial prototype such obtained showed better result than the randomly selected one commonly used in FCM, which was demonstrated by experimental results.
Keywords
Clustering; Fuzzy C-Means; Initial Prototype Selection; Kernel Density Estimation;
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