A modified brainstorm optimization for clustering using hard c-means
Reetika Roy, J. Anuradha · 2015
The preeminent intention of the proposed study is exploring the performance of the Brainstorm Optimization algorithm in Hard c-means clustering of data. The rationale behind this analysis is to generate a random solution set of centroids and then modify the centroids so as to refine the clusters. As we are using Brainstorm Optimization which is a form of evolutionary algorithm this refinement of centroid happens through competition and cooperation with existing centroid values. This algorithm incorporates both exploitation and exploration of the search space to generate the new centroids. The algorithm has been implemented with the Iris data set and its validity and effectiveness is tested with the help of commonly used internal evaluation measures for clustering like Davies Boudlin Index and Dunn Index.