A new approach to clustering data with arbitrary shapes

Mu Chun Su, Yi Chun Liu

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like training algorithm is used to cluster data into a set of multi-dimensional hyperellipsoids. At the second stage, a dendrogram is built to complement the neural network. We then use dendrograms and so-called tables of relative frequency counts to help analysts to pick some trustable clustering results from a lot of different clustering results. Several data sets were tested to demonstrate the performance of the proposed algorithm.

Original languageEnglish
Pages (from-to)1887-1901
Number of pages15
JournalPattern Recognition
Issue number11
StatePublished - Nov 2005


  • ART
  • Cluster analysis
  • Clustering
  • Hierarchical partitioning
  • Unsupervised learning


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