C-AP

Abstract

Affinity Propagation is one of the fundamental clustering algorithms used in various Web-based systems and applications. Although Affinity Propagation can find highly accurate clusters, it is computationally expensive to apply Affinity Propagation to a large dataset since it requires to iteratively compute all possible pairs of data objects in the dataset. In this paper, we propose a novel Affinity Propagation algorithm named C-AP for tackling this problem. Towards the problem, C-AP employs cell-based index to reduce the number of the computed pairs in the clustering procedure. By using the cell-based index, C-AP efficiently detects unnecessary pairs, which do not contribute to its clustering result. As a result, C-AP detects the same clusters as those of Affinity Propagation with much shorter computation time. Extensive evaluations demonstrate the performance superiority of C-AP over the state-of-the-art algorithms.