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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

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dc.citedby1
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorAl-amri R.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorGhorashi S.en_US
dc.contributor.authorAlabdulkreem E.en_US
dc.contributor.authorKiong Tiong S.en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid57224896623en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid57219241229en_US
dc.contributor.authorid55320872600en_US
dc.contributor.authorid57219799117en_US
dc.date.accessioned2024-10-14T03:18:28Z
dc.date.available2024-10-14T03:18:28Z
dc.date.issued2023
dc.description.abstractIn a world of connectivity empowered by the advancement of the Internet of Things (IoT), an infinite number of data streams have emerged. Thus, data stream clustering is crucial for extracting hidden knowledge and data mining. Various data stream clustering methods have lately been introduced. Yet, the majority of such algorithms are affected by the curse of high dimensionality. Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. Similarly to other existing density-based clustering methods, BOCEDS is not capable of handling high-dimensional data and has high computational power and high memory utilization. This paper introduces an Entropy Window Reduction (EWR) algorithm, which is an improved version of the BOCEDS technique. EWR is a fully online clustering technique for handling high-dimensional data streams using feature ranking and sorting. This process is accomplished by calculating the entropy of specific features with respect to the time window. The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. The results are demonstrated using the KDDCup�99 dataset in terms of quality and complexity evaluation on the average of F-Measures, Jaccard Index, Fowlkes�Mallows index, Purity, and Rand Index as well as the memory usage and computational power with 88%, 66%, 81%, 100%, and 66%, respectively. The results also show low memory usage and computing power in comparison with the baseline algorithms. � 2023 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2023.03.008
dc.identifier.epage513
dc.identifier.scopus2-s2.0-85149833939
dc.identifier.spage503
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85149833939&doi=10.1016%2fj.aej.2023.03.008&partnerID=40&md5=5c75f128e66ab093f54c6a54183098d4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34215
dc.identifier.volume70
dc.pagecount10
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectAnomaly detection
dc.subjectClustering
dc.subjectComputational power
dc.subjectData stream
dc.subjectEntropy
dc.subjectHigh-dimensionality
dc.subjectIoT
dc.subjectAnomaly detection
dc.subjectCluster analysis
dc.subjectClustering algorithms
dc.subjectData mining
dc.subjectInternet of things
dc.subjectPetroleum reservoir evaluation
dc.subject% reductions
dc.subjectAnomaly detection
dc.subjectClustering methods
dc.subjectClusterings
dc.subjectComputational power
dc.subjectData stream
dc.subjectData stream clustering
dc.subjectHigh dimensionality
dc.subjectHigh-dimensional data streams
dc.subjectReduction algorithms
dc.subjectEntropy
dc.titleTowards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reductionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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