Leaders-subleaders: an efficient hierarchical clustering algorithm for large data sets

  • Authors:
  • P. A. Vijaya;M. Narasimha Murty;D. K. Subramanian

  • Affiliations:
  • Department of Computer Science and Automation, Intelligent Systems Lab, Indian Institute of Science, Bangalore 560 012, India;Department of Computer Science and Automation, Intelligent Systems Lab, Indian Institute of Science, Bangalore 560 012, India;Department of Computer Science and Automation, Intelligent Systems Lab, Indian Institute of Science, Bangalore 560 012, India

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2004

Quantified Score

Hi-index 0.10

Visualization

Abstract

In this paper, an efficient hierarchical clustering algorithm, suitable for large data sets is proposed for effective clustering and prototype selection for pattern classification. It is another simple and efficient technique which uses incremental clustering principles to generate a hierarchical structure for finding the subgroups/subclusters within each cluster. As an example, a two level clustering algorithm--'Leaders-Subleaders', an extension of the leader algorithm is presented. Classification accuracy (CA) obtained using the representatives generated by the Leaders-Subleaders method is found to be better than that of using leaders as representatives. Even if more number of prototypes are generated, classification time is less as only a part of the hierarchical structure is searched.