Elsevier

Pattern Recognition Letters

Volume 15, Issue 9, September 1994, Pages 893-899
Pattern Recognition Letters

How to choose a representative subset from a set of data in multi-dimensional space

https://doi.org/10.1016/0167-8655(94)90151-1Get rights and content

Abstract

Given a set of N points in multi-dimensional space, it may be necessary to choose a subset of n representative points. For example, in clustering problems, it is necessary to choose a few seed points around which the cluster may grow. This problem may be posed as that of choosing one out of each k data when ⌊N/n⌋ = k. In our proposed method, the data points are ordered in decreasing magnitude of density. The datum toping the ordered list is chosen and its k − 1 nearest neighbours are deleted from the ordered list. From the remaining data, the one currently toping the list is chosen. The process is repeated till the data are exhausted. The problem of more general choice of n is also addressed.

References (6)

  • M.M. Astrahan

    Speech analysis by clustering, or the hyperphomene method

  • G.H. Ball et al.

    PROMENADE, An one-line Pattern Recognition

  • D. Chaudhuri et al.

    A minimum spanning tree based probability density estimation procedure

    IEEE Trans. Pattern Anal. Machine Intell.

    (1993)
There are more references available in the full text version of this article.

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