Fuzzy C strange points clustering algorithm
Terence Johnson, Santosh Kumar Singh · 2016
The Fuzzy C Strange (FCS) points clustering algorithm takes a broad view of the Enhanced K Strange points clustering algorithm to permit a point to partly fit in to various clusters thereby generating a soft categorization for a given set of elements. To achieve this, the objective function of the hard Enhanced K Strange has been drawn-out by incorporating fuzzy membership degrees in clusters into the formula and an additional parameter p was brought in as a weight proponent in the fuzzy membership. The proposed algorithm was found to give a better quality of clusters than the Fuzzy C Means and the K Means clustering methods.