On cluster tree for nested and multi-density data clustering

Authors:
Xutao Li;Yunming Ye;Mark Junjie Li;Michael K. Ng
Affiliations:
Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, China;Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, China;Institute of Advanced Computing and Digital Engineering, Centre for High Performance Computing Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR Chin ...;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Venue:
Pattern Recognition
Year:
2010

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Abstract

Clustering is one of the important data mining tasks. Nested clusters or clusters of multi-density are very prevalent in data sets. In this paper, we develop a hierarchical clustering approach-a cluster tree to determine such cluster structure and understand hidden information present in data sets of nested clusters or clusters of multi-density. We embed the agglomerative k-means algorithm in the generation of cluster tree to detect such clusters. Experimental results on both synthetic data sets and real data sets are presented to illustrate the effectiveness of the proposed method. Compared with some existing clustering algorithms (DBSCAN, X-means, BIRCH, CURE, NBC, OPTICS, Neural Gas, Tree-SOM, EnDBSAN and LDBSCAN), our proposed cluster tree approach performs better than these methods.