Parallel clustering by fast search and find of density peaks

Abstract

The algorithm clustering by fast search and find of density peaks shows good efficiency and accuracy, but the space complexity of the algorithm is too high since it has to keep a global distance matrix in memory, so it can hardly process big dataset clustering. To solve this problem, this paper designed a new strategy for the algorithm to search the important quantity δ, by using the new strategy, the space complexity of the algorithm is greatly reduced. And based on that reduction, a corresponding load balanced parallel clustering algorithm was presented in this paper, experimental results show that the parallel algorithm is efficient and scalable.