Minimum spanning tree based split-and-merge: A hierarchical clustering method

Authors:
Caiming Zhong;Duoqian Miao;Pasi Fränti
Affiliations:
Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China and Department of Computer Science, University of Eastern Finland, P.O. Box: 111, FIN-80101 Joensuu, Fin ...;Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China;Department of Computer Science, University of Eastern Finland, P.O. Box: 111, FIN-80101 Joensuu, Finland
Venue:
Information Sciences: an International Journal
Year:
2011

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Abstract

Most clustering algorithms become ineffective when provided with unsuitable parameters or applied to datasets which are composed of clusters with diverse shapes, sizes, and densities. To alleviate these deficiencies, we propose a novel split-and-merge hierarchical clustering method in which a minimum spanning tree (MST) and an MST-based graph are employed to guide the splitting and merging process. In the splitting process, vertices with high degrees in the MST-based graph are selected as initial prototypes, and K-means is used to split the dataset. In the merging process, subgroup pairs are filtered and only neighboring pairs are considered for merge. The proposed method requires no parameter except the number of clusters. Experimental results demonstrate its effectiveness both on synthetic and real datasets.