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dc.contributor.authorHassan, BA
dc.contributor.authorRashid, TA
dc.contributor.authorMirjalili, S
dc.date.accessioned2021-05-10T00:56:37Z
dc.date.available2021-05-10T00:56:37Z
dc.date.issued2021
dc.identifier.issn2352-3409
dc.identifier.doi10.1016/j.dib.2021.107044
dc.identifier.urihttp://hdl.handle.net/10072/404216
dc.description.abstractThis article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom107044
dc.relation.ispartofjournalData in Brief
dc.relation.ispartofvolume36
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode08
dc.titlePerformance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationHassan, BA; Rashid, TA; Mirjalili, S, Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets, Data in Brief, 2021, 36, pp. 107044
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-05-09T23:00:53Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 The Authors. Published by Elsevier Inc. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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