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dc.contributor.authorMusayeva, Khadija
dc.contributor.authorHenderson, Tristan
dc.contributor.authorMitchell, John B. O.
dc.contributor.authorMavridis, Lazaros
dc.date.accessioned2014-03-07T17:31:01Z
dc.date.available2014-03-07T17:31:01Z
dc.date.issued2014-02
dc.identifier.citationMusayeva , K , Henderson , T , Mitchell , J B O & Mavridis , L 2014 , ' PFClust: an optimised implementation of a parameter-free clustering algorithm ' , Source Code for Biology and Medicine , vol. 9 , no. 5 . https://doi.org/10.1186/1751-0473-9-5en
dc.identifier.issn1751-0473
dc.identifier.otherPURE: 95083033
dc.identifier.otherPURE UUID: aecc218f-8405-4fc0-aa16-fd2148dc98c9
dc.identifier.otherBibtex: urn:68778f010ac5a1f4c961a557986f9f0d
dc.identifier.otherScopus: 84893200342
dc.identifier.otherORCID: /0000-0002-0379-6097/work/34033394
dc.identifier.urihttps://hdl.handle.net/10023/4491
dc.descriptionThis work was supported by the World Anti-Doping Agency and the Scottish Universities Life Sciences Alliance.en
dc.description.abstractBackground: A well-known problem in cluster analysis is finding an optimal number of clusters reflecting the inherent structure of the data. PFClust is a partitioning-based clustering algorithm capable, unlike many widely-used clustering algorithms, of automatically proposing an optimal number of clusters for the data. Results: The results of tests on various types of data showed that PFClust can discover clusters of arbitrary shapes, sizes and densities. The previous implementation of the algorithm had already been successfully used to cluster large macromolecular structures and small druglike compounds. We have greatly improved the algorithm by a more efficient implementation, which enables PFClust to process large data sets acceptably fast. Conclusions: In this paper we present a new optimized implementation of the PFClust algorithm that runs considerably faster than the original.
dc.language.isoeng
dc.relation.ispartofSource Code for Biology and Medicineen
dc.rights© 2014 Musayeva et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectClusteringen
dc.subjectCluster analysisen
dc.subjectNumber of clustersen
dc.titlePFClust: an optimised implementation of a parameter-free clustering algorithmen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. EaSTCHEMen
dc.identifier.doihttps://doi.org/10.1186/1751-0473-9-5
dc.description.statusPeer revieweden


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