Automatic shell clustering using a metaheuristic approach

This paper proposes a simple, metaheuristic clustering technique, inspired by the mountain clustering method of Yager and Filev, for detecting general quadric shell type clusters. The algorithm employs an ecologically inspired metaheurisitc algorithm, called Invasive Weed Optimization (IWO) to evolve a set of cluster prototypes in the shape of curves/hyper-surfaces. The objective function is modeled using the concept of the mountain function from Yager and Filev´s work. The metaheuristic approach can be extended to solid clusters and various shell clusters like circular, elliptical, rectangular etc. The proposed method is tested on several synthetic datasets as well as real images to detect circular and elliptical shell clusters and the results obtained are found to be very promising.