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A comparative study of major clustering techniques for MAR learning usability prioritization processes

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dc.contributor.authorLim K.C.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorMohamed Zabil M.H.en_US
dc.contributor.authorSelamat M.H.en_US
dc.contributor.authorAlias R.A.en_US
dc.contributor.authorMohamed F.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid57215520379en_US
dc.contributor.authorid25928253600en_US
dc.contributor.authorid55416008900en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T08:07:34Z
dc.date.available2023-05-29T08:07:34Z
dc.date.issued2020
dc.descriptionAugmented reality; Hierarchical clustering; Usability engineering; Clustering techniques; Comparative studies; K-means; Mobile augmented reality; Mode-based; Prioritization process; Related works; Research methodologies; Iterative methodsen_US
dc.description.abstractThis paper presents and discusses a comparative study of three major clustering categories namely Hierarchical-based, Iterative mode-based and Partition-based in analyzing and prioritizing Mobile Augmented reality (MAR) Learning (MAR-learning) usability data. This paper first discusses the related works in usability and clustering before moving on to the identification of gaps that can be addressed through experimentation. This paper will then propose a research methodology to measure four common clustering techniques on MAR-learning usability data. The paper will then discourse comparative results showing how Mini-batch K-means to be an ideal technique within the experimental setup. The paper will then present important research highlights, discussion, conclusion and future works. � 2020 The authors and IOS Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/FAIA200577
dc.identifier.epage329
dc.identifier.scopus2-s2.0-85092743262
dc.identifier.spage317
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092743262&doi=10.3233%2fFAIA200577&partnerID=40&md5=c0adf9d866691ea74410ca1f4c8067eb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25248
dc.identifier.volume327
dc.publisherIOS Press BVen_US
dc.sourceScopus
dc.sourcetitleFrontiers in Artificial Intelligence and Applications
dc.titleA comparative study of major clustering techniques for MAR learning usability prioritization processesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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