A hybrid clustering procedure for concentric and chain-like clusters

  title={A hybrid clustering procedure for concentric and chain-like clusters},
  author={M. Narasimha Murty and G. Krishna},
  journal={International Journal of Computer & Information Sciences},
K-means algorithm is a well known nonhierarchical method for clustering data. The most important limitations of this algorithm are that: (1) it gives final clusters on the basis of the cluster centroids or the seed points chosen initially, and (2) it is appropriate for data sets having fairly isotropic clusters. But this algorithm has the advantage of low computation and storage requirements. On the other hand, hierarchical agglomerative clustering algorithm, which can cluster nonisotropic… CONTINUE READING
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Clustering algorithms (Wiley

  • J. A. Hartigan
  • New York,
  • 1975
Highly Influential
4 Excerpts

PROMENADE--An outline pattern recognition system," RADC-TR-67-310, AD

  • G. H. Ball, J D., Hall
  • 1967
Highly Influential
3 Excerpts

A FORTRAN IV iterative K-means cluster analysis program," Behavioral Science, Vol

  • D. J. McRae, MIKCA
  • 16, pp. 423
  • 1971
1 Excerpt

Astrahn, "Speech analysis by clustering or the hyperphoneme method," Stanford artificial intelligence project, AD 709067 (Stanford

  • M M.
  • 1970

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