A Hierarchical Clustering Algorithm Based on Saturated Neighbor Graph

Abstract

This paper proposes a hierarchical clustering algorithm based on Saturated Neighbor Graph -- hi-CLUBS and a new concept, natural nearest neighbor, which adopts a parameter-less algorithm of searching the natural neighbors for each point in a dataset. In the work, the Saturated Neighbor Graph is constructed by the natural nearest neighbor firstly. Then modularity is introduced into graph partitioning algorithm, with which the generated graph is partitioned into small sub-clusters without any parameters. Finally, these initial sub-clusters are repeatedly merged with another cluster according to similarity measurement based on connectivity and closeness, until the desired cluster number is reached. The results show that hi-CLUBS produces a set of final clusters achieves better quality than the traditional clustering algorithms.