The redesigned Fuzzy C Strange points clustering algorithm

Abstract

The redesigned Fuzzy C Strange points clustering algorithm uses the membership function to find the strange points and also to establish the degree of likeness of elements to different clusters as opposed to the traditional fuzzy c strange points clustering algorithm which uses the Euclidean distance to find the strange points and membership function only to group the points into clusters. The redesigned algorithm was observed to give similar quality of clusters and also converge with the same speed of execution as the orthodox fuzzy c strange points clustering method.