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Clustering based on median and closest string via rank distance with applications on DNA

  • ICONIP 2012
  • Published:
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Abstract

This paper aims to present several clustering methods based on rank distance. Rank distance has applications in many different fields such as computational linguistics, biology and computer science. The K-means algorithm represents each cluster by a single mean vector. The mean vector is computed with respect to a distance measure. Two K-means algorithms based on rank distance are described in this paper. Hierarchical clustering builds models based on distance connectivity. This paper describes two hierarchical clustering techniques that use rank distance. Experiments using mitochondrial DNA sequences extracted from several mammals are performed to compare the results of the clustering methods. Results demonstrate the clustering performance and the utility of the proposed algorithms.

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Acknowledgments

The contribution of the authors to this paper is equal. Authors thank anonymous reviewers for helpful comments. The research of Liviu P. Dinu was supported by a grant of the Romanian National Authority for Scientific Research, CNCS UEFISCDI, project number PN-II-ID-PCE-2011-3-0959. Radu Tudor Ionescu thanks his Ph.D. supervisor Denis Enachescu from the University of Bucharest, for helpful discussions.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Bucharest, No. 14 Academiei Street, Bucharest, Romania

    Liviu P. Dinu & Radu Tudor Ionescu

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  1. Liviu P. Dinu
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  2. Radu Tudor Ionescu
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Correspondence to Radu Tudor Ionescu.

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Dinu, L.P., Ionescu, R.T. Clustering based on median and closest string via rank distance with applications on DNA. Neural Comput & Applic 24, 77–84 (2014). https://doi.org/10.1007/s00521-013-1468-x

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