An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods

Abstract In this paper, we introduce a new evidential clustering algorithm based on finding the belief-peaks and disjoint neighborhoods, called BPDNEC. The basic idea of BPDNEC is that each cluster center has the highest possibility of becoming a cluster center among its neighborhood and neighborhoods of those cluster centers are disjoint in vector space. Such possibility is measured by the belief notion in framework of evidence theory. By solving an equation related to neighborhood size, the size of such disjoint neighborhoods is determined and those objects having highest belief among their neighborhoods are automatically detected as cluster centers. Finally, a credal partition is created by minimizing an objective function concerning dissimilarity matrix of data objects. Experimental results show that BPDNEC can automatically detect cluster centers and derive an appropriate credal partition for both object data and proximity data. Simulations on synthetic and real-world datasets validate the conclusions.