Department of Mathematics
On cluster tree for nested and multi-density data clustering
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. © 2010 Elsevier Ltd. All rights reserved.
Cluster tree, Hierarchical clustering, K-Means-type algorithm, Multi-densities
Source Publication Title
Li, Xutao, Yunming Ye, Mark Junjie Li, and Michael K. Ng. "On cluster tree for nested and multi-density data clustering." Pattern Recognition 43.9 (2010): 3130-3143.