HDenDist: Nonlinear Hierarchical Clustering Based on Density and Min-distance

Hierarchical clustering has received a great amount of attention due to the capability of capturing hierarchical cluster structure in an unsupervised way. Despite great success, most of the existing hierarchical clustering algorithms have some drawbacks: (1) difficulty in selecting clusters to merge or split, (2) inefficient and inaccurate cluster validation, (3) limitation to only linearly separable clusters. To address the above issues, this paper proposes a new nonlinear hierarchical clustering method termed HDenDist. The proposed method is based on two observations/designs associated with density and min-distance. One is that cluster centers have a higher density and are surrounded by data points of lower density, and the distance between cluster centers is relatively long, the other is that we design a min-distance between nodes, which can be used to determine how to divide the nodes in the hierarchical tree into two sub-cluster nodes. Some dividing and ruling tricks are designed that can further reduce the sensitivity to parameters. What´s more, the density and distance are combined to determine when to terminate the split of the cluster nodes. In experimental studies, the proposed method has shown promising results on real datasets.