On cluster tree for nested and multi-density data clustering

Xutao Li, Yunming Ye, Mark Junjie Li, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3130-3143
Number of pages14
JournalPattern Recognition
Volume43
Issue number9
DOIs
Publication statusPublished - Sep 2010

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Cluster tree
  • Hierarchical clustering
  • K-Means-type algorithm
  • Multi-densities

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