P. Murugeswari,




Cluster validity indices,IT2FCM,Extended IT2FCM,IT2FCMα,


In recent years several classification techniques have been proposed which are classified into supervised and unsupervised classifications. In unsupervised classification, fuzzy clustering analysis is a most common technique since it never needs training data for fuzzy clustering algorithm. Nevertheless, different clustering algorithms have different initial conditions to generate different partitions and use different parameters in order to produce different results. Thus, the partitions generated by fuzzy clustering algorithm are in need to validate. Many cluster validity indices have been proposed in the last three decades for validating type-1 fuzzy based FCM algorithm. Recently many type-2 fuzzy based applications were presented due to its extract degree of fuzziness. But its computational complexity is very high, so interval type-2 fuzzy system is widely used in many applications. After the updation of cluster centriods in type-2 fuzzy based FCM algorithm, the   type-2 fuzzy membership function is taken as unreliability of type-1 membership function. Therefore there is a need for a new method to validate the cluster validity index for interval type-2 fuzzy system based applications. In this paper, we have presented a new approach of validating the 14 cluster validity indices and performed extensive comparison of the mentioned indices in conjunction with various interval type-2 fuzzy c-means clustering algorithms. For experimental analysis we have taken the number of widely used datasets and Berkely image database. 


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