Identification of optimal cluster centroid for unconstrained nonlinear multivariable functions

Identification of useful clusters in large datasets has attracted considerable interest in clustering process. Since data in the World Wide Web is increasing exponentially that affects on clustering accuracy and decision making, change in the concept between every cluster occurs named concept drift. To perfectly handle these drifting concepts, assigning new data to existing cluster must be performed called data labeling. For efficient data labeling the existing clusters must be efficient. Selecting initial cluster center (centroid) is the key factor that has high affection in generating effective clusters. The insufficiency of traditional clustering methods in selecting initial cluster center has been motivated towards this work. Our previous work focus on selecting optimal cluster centroid for multivariable functions that does not require gradient information. This paper extends selecting optimal cluster centroid for unconstrained nonlinear multivariable gradient functions and then apply any existing clustering algorithm.