Publication:
A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET

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dc.citedby4
dc.contributor.authorAbdulrazzak H.N.en_US
dc.contributor.authorHock G.C.en_US
dc.contributor.authorMohamed Radzi N.A.en_US
dc.contributor.authorTan N.M.L.en_US
dc.contributor.authorid57210449807en_US
dc.contributor.authorid16021614500en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid24537965000en_US
dc.date.accessioned2024-10-14T03:21:01Z
dc.date.available2024-10-14T03:21:01Z
dc.date.issued2023
dc.description.abstractClustering evaluation techniques are important to check the clustering algorithm quality. High cluster similarity help to reduce the distance between a node to node within the cluster, also good separation was more important to avoid overlapping clusters. The network performance will increase and the signal will be high. Many researchers proposed different validation indexes such as Davies-Bouldin, Dunn, and Silhouette indexes. These cluster validation indexes focus on the internal or external cluster similarity, and some of them deal with both cases. The employing of graph-based distance to non-spherical clusters and selection of reference points will not be effective all the time because the average distance between reference points and all nodes will be changed dynamically such as in the VANET application. To solve this problem a dynamic sample node should be selected or the similarity of all nodes should be checked. This paper proposes a new Minimum intra-distance and Maximum inter-distance Index (M2I) to improve these indexes. The proposed index checks the internal similarity and the external distance among all nodes from cluster to cluster to ensure that high separation will occur. M2I checks the similarity from node to node within the cluster and cluster to cluster. The proposed index will be an improvement of all high-rank indexes. The proposed index was applied in different scenarios (VANET and real datasets scenarios) and compared with other indexes. The index result shows that the proposed M2I outperforms the others. The M2I accuracy is 100% in the VANET scenario and 89% in the real datasets scenario. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3281302
dc.identifier.epage67555
dc.identifier.scopus2-s2.0-85161088098
dc.identifier.spage67540
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161088098&doi=10.1109%2fACCESS.2023.3281302&partnerID=40&md5=376ff1ba1b1e0901f1ef2199010cb64a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34604
dc.identifier.volume11
dc.pagecount15
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectcluster index validation
dc.subjectClustering analysis
dc.subjectenergy clustering algorithms
dc.subjectK-means
dc.subjectunsupervised learning
dc.subjectVANET clustering
dc.subjectCluster analysis
dc.subjectEnergy efficiency
dc.subjectGraphic methods
dc.subjectHeuristic algorithms
dc.subjectQuality control
dc.subjectUnsupervised learning
dc.subjectVehicular ad hoc networks
dc.subjectCluster index validation
dc.subjectClustering analysis
dc.subjectClusterings
dc.subjectEnergy
dc.subjectEnergy clustering algorithm
dc.subjectHeuristics algorithm
dc.subjectIndex
dc.subjectK-means
dc.subjectPartitioning algorithms
dc.subjectVANET clustering
dc.subjectVehicular Adhoc Networks (VANETs)
dc.subjectClustering algorithms
dc.titleA New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANETen_US
dc.typeArticleen_US
dspace.entity.typePublication
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