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

Clustering 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 th...

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Main Authors: Abdulrazzak H.N., Hock G.C., Mohamed Radzi N.A., Tan N.M.L.
Other Authors: 57210449807
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
cluster index validation
Clustering analysis
energy clustering algorithms
K-means
unsupervised learning
VANET clustering
Cluster analysis
Energy efficiency
Graphic methods
Heuristic algorithms
Quality control
Unsupervised learning
Vehicular ad hoc networks
Cluster index validation
Clusterings
Energy
Energy clustering algorithm
Heuristics algorithm
Index
Partitioning algorithms
Vehicular Adhoc Networks (VANETs)
Clustering algorithms
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spelling my.uniten.dspace-346042024-10-14T11:21:01Z A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET Abdulrazzak H.N. Hock G.C. Mohamed Radzi N.A. Tan N.M.L. 57210449807 16021614500 57218936786 24537965000 cluster index validation Clustering analysis energy clustering algorithms K-means unsupervised learning VANET clustering Cluster analysis Energy efficiency Graphic methods Heuristic algorithms Quality control Unsupervised learning Vehicular ad hoc networks Cluster index validation Clustering analysis Clusterings Energy Energy clustering algorithm Heuristics algorithm Index K-means Partitioning algorithms VANET clustering Vehicular Adhoc Networks (VANETs) Clustering algorithms Clustering 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. Final 2024-10-14T03:21:01Z 2024-10-14T03:21:01Z 2023 Article 10.1109/ACCESS.2023.3281302 2-s2.0-85161088098 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161088098&doi=10.1109%2fACCESS.2023.3281302&partnerID=40&md5=376ff1ba1b1e0901f1ef2199010cb64a https://irepository.uniten.edu.my/handle/123456789/34604 11 67540 67555 All Open Access Gold Open Access Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic cluster index validation
Clustering analysis
energy clustering algorithms
K-means
unsupervised learning
VANET clustering
Cluster analysis
Energy efficiency
Graphic methods
Heuristic algorithms
Quality control
Unsupervised learning
Vehicular ad hoc networks
Cluster index validation
Clustering analysis
Clusterings
Energy
Energy clustering algorithm
Heuristics algorithm
Index
K-means
Partitioning algorithms
VANET clustering
Vehicular Adhoc Networks (VANETs)
Clustering algorithms
spellingShingle cluster index validation
Clustering analysis
energy clustering algorithms
K-means
unsupervised learning
VANET clustering
Cluster analysis
Energy efficiency
Graphic methods
Heuristic algorithms
Quality control
Unsupervised learning
Vehicular ad hoc networks
Cluster index validation
Clustering analysis
Clusterings
Energy
Energy clustering algorithm
Heuristics algorithm
Index
K-means
Partitioning algorithms
VANET clustering
Vehicular Adhoc Networks (VANETs)
Clustering algorithms
Abdulrazzak H.N.
Hock G.C.
Mohamed Radzi N.A.
Tan N.M.L.
A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
description Clustering 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.
author2 57210449807
author_facet 57210449807
Abdulrazzak H.N.
Hock G.C.
Mohamed Radzi N.A.
Tan N.M.L.
format Article
author Abdulrazzak H.N.
Hock G.C.
Mohamed Radzi N.A.
Tan N.M.L.
author_sort Abdulrazzak H.N.
title A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
title_short A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
title_full A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
title_fullStr A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
title_full_unstemmed A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
title_sort new unsupervised validation index model suitable for energy-efficient clustering techniques in vanet
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061129302802432
score 13.235362

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