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dc.contributor.authorZogheib, Bashar
dc.date.accessioned2020-04-10T14:16:58Z
dc.date.available2020-04-10T14:16:58Z
dc.date.issued2019
dc.identifier.urihttps://doi.org/10.1080/24709360.2019.1615770
dc.identifier.urihttps://dspace.auk.edu.kw/handle/11675/5752
dc.description.abstractFinding the number of clusters in a data set is considered as one of the fundamental problems in cluster analysis. This paper integrates maximum clustering similarity (MCS), for finding the optimal number of clusters, into R©statistical software through the package MCSim. The similarity between the two clustering methods is calculated at the same number of clusters, using Rand [Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66:846–850.] and Jaccard [The distribution of the flora of the alpine zone. New Phytologist. 1912;11:37–50.] indices, corrected for chance agreement. The number of clusters at which the index attains its maximum with most frequency is a candidate for the optimal number of clusters. Unlike other criteria, MCS can be used with circular data. Seven clustering algorithms, existing in R©, are implemented in MCSim. A graph of the number of clusters vs. clusters similarity using corrected similarity indices is produced. Values of the similarity indices and a clustering tree (dendrogram) are produced. Several examples including simulated, real, and circular data sets are presented to show how MCSim successfully works in practice.
dc.publisherTaylor & Francis
dc.relation.journalBiostatistics and Epidemiology
dc.titleHow many clusters exist? Answer via maximum clustering similarity implemented in R
dc.typeJournal Article
dcterms.bibliographicCitationAlbatineh, A. N., Wilcox,M. L., Zogheib, B., & Niewiadomska-Bugaj, M. (2019). How many clustersexist? Answer via maximum clustering similarity implemented in R. Biostatistics& Epidemiology, 3(1), 62-79.
dc.journal.volume3
dc.journal.issue1
dc.article.pages62-79


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