Spatial Bias Correction Based on Gaussian Kernel Fuzzy C Means in Clustering
نویسنده
چکیده
Clustering is the process of grouping data objects into set of disjointed classes called clusters so that objects within a class are highly similar to one another and dissimilar to the objects in other classes. K-means (KM) and Fuzzy c-means (FCM) algorithms are popular and powerful methods for cluster analysis. However, the KM and FCM algorithms have considerable trouble in a noisy environment and are inaccurate with large numbers of different sample sized clusters. The Kernel based Fuzzy C-Means (KFCM) clustering is moreover studied with associated cluster validity measures. Many numerical simulations are used to evaluate whether or not the kernelized measures are adequate for ordinary ball-shaped clusters. Finally, a new class of kernel functions is Gaussian kernel based Fuzzy C-Means (GKFCM) is proposed in this research. The proposed GKFCM algorithm becomes a generalized type of, BCFCM, and KFCM algorithms and presents with more efficiency and robustness.
منابع مشابه
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Article history: Received 13 September 2006 Received in revised form 28 February 2008 Available online 9 May 2008 Communicated by A.M. Alimi
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