A hybrid clustering procedure for concentric and chain-like clusters (original) (raw)

Abstract

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 (chain-like and concentric) clusters, requires high storage and computation requirements. This paper suggests a new method for selecting the initial seed points, so that the_K-means algorithm gives the same results for any input data order. This paper also describes a hybrid clustering algorithm, based on the concepts of multilevel theory, which is nonhierarchical at the first level and hierarchical from second level onwards, to cluster data sets having (i) chain-like clusters and (ii) concentric clusters. It is observed that this hybrid clustering algorithm gives the same results as the hierarchical clustering algorithm, with less computation and storage requirements.

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Authors and Affiliations

  1. School of Automation, Indian Institute of Science, 560012, Bangalore, India
    M. Narasimha Murty & G. Krishna

Authors

  1. M. Narasimha Murty
  2. G. Krishna

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Murty, M.N., Krishna, G. A hybrid clustering procedure for concentric and chain-like clusters.International Journal of Computer and Information Sciences 10, 397–412 (1981). https://doi.org/10.1007/BF00996137

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