Abstract: Fuzzy C means(FCM) algorithm is the main algorithm for data clustering analysis.But in a noisy environment,for the clusters of different sampling sizes,accuracy is low when the number of clusters is large.The above disadvantages can be sloved through the Gauss kernel mapping of alternative FCM(AFCM) .This paper proposed generalized Lorenz kernel function to the fuzzy C means clustering for the deficiency of AFCM. This algorithm was used to analyze the Iris database cluster,to classify the Iris database into three clusters of Iris setosa,Iris versicolour and Iris virginica.Experimental results show that the generalized lorentzian fuzzy C-means(GLFCM) can classify data of outliers and un-equal sized clusters.The GLFCM yields better cluster than K-means(KM),FCM,alternative fuzzy C-means(AFCM),Gustafson-Kessel(GK) and Gath-Geva(GG).It takes less iteration than that of AFCM to converge.Its partition index(SC) is better than the others.

Key words: Generalized lorentzian membership function,K-means,Alternative fuzzy C-means,Clustering,Outlier clustering