A fast hierarchical clustering approach based on partition and merging scheme

Hierarchical clustering is one major kind of clustering approaches. As far as we know, given n data points, the time complexity of most existing hierarchical clustering approaches is O(n). Although some state-of-the-art fast hierarchical clustering approaches have lower time complexity, their clustering accuracy is sacrificed and sensitive to some certain data distribution types. This paper therefore presents a partition-and-merging scheme for fast hierarchical clustering, which divides data objects into proper groups and merges them within their groups to save computation cost. Since both spatial distance and density difference, which contain local and global distribution information of data, are considered in the merging stage, the proposed approach has outstanding performance in terms of effectiveness, efficiency and robustness. Experimental results show the promising results in comparison with the existing counterparts.