A KNN-scoring based core-growing approach to cluster analysis

T. W. Hsieh, J. S. Taur, S. Y. Kung

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

This paper proposes a novel core-growing (CG) clustering method based on scoring k-nearest neighbors (CG-KNN). First, an initial core for each cluster is obtained, and then a tree-like structure is constructed by sequentially absorbing data points into the existing cores according to the KNN linkage score. The CG-KNN can deal with arbitrary cluster shapes via the KNN linkage strategy. On the other hand, it allows the membership of a previously assigned training pattern to be changed to a more suitable cluster. This is supposed to enhance the robustness. Experimental results on four UCI real data benchmarks and Leukemia data sets indicate that the proposed CG-KNN algorithm outperforms several popular clustering algorithms, such as Fuzzy C-means (FCM) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645-678, 2005), Hierarchical Clustering (HC) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645-678, 2005), Self-Organizing Maps (SOM) (Golub et al. Science 286:531-537, 1999; Tamayo et al. Proceedings of the National Academy of Science USA 96:2907, 1999), and Non-Euclidean Norm FCM (NEFCM) (Karayiannis and Randolph-Gips IEEE Transactions On Neural Networks 16, 2005).

Original languageEnglish (US)
Pages (from-to)105-114
Number of pages10
JournalJournal of Signal Processing Systems
Volume60
Issue number1
DOIs
StatePublished - Jul 1 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture

Keywords

  • Clustering method
  • Core-growing
  • K-nearest neighbor

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