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A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

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dc.contributor.authorAl?amri R.en_US
dc.contributor.authorMurugesan R.K.en_US
dc.contributor.authorAlmutairi M.en_US
dc.contributor.authorMunir K.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorid57224896623en_US
dc.contributor.authorid57198406478en_US
dc.contributor.authorid57672164400en_US
dc.contributor.authorid57671857800en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid56768090200en_US
dc.date.accessioned2023-05-29T09:37:05Z
dc.date.available2023-05-29T09:37:05Z
dc.date.issued2022
dc.description.abstractAs applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering�s primary objective and goal are to acquire insights into incoming data. Recogniz-ing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core?Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo?spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo?spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer?based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering met-rics. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6523
dc.identifier.doi10.3390/app12136523
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85133342460
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85133342460&doi=10.3390%2fapp12136523&partnerID=40&md5=251ccab371644faa62602a585878788b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26836
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleA Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cubeen_US
dc.typeArticleen_US
dspace.entity.typePublication
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