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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2014

01.01.2014 | ICONIP 2012

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

verfasst von: Liviu P. Dinu, Radu Tudor Ionescu

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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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.
Vorheriger Artikel Support vector machines for surrogate modeling of electronic circuits
Nächster Artikel Current–voltage modeling of graphene-based DNA sensor

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Metadaten
Titel
Clustering based on median and closest string via rank distance with applications on DNA
verfasst von
Liviu P. Dinu
Radu Tudor Ionescu
Publikationsdatum
01.01.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1468-x

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