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

In 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,...

Full description

Saved in:
Bibliographic Details
Main Authors: Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.
Other Authors: 57191982354
Format: Article
Published: Elsevier B.V. 2024
Subjects:
IoT
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-34215
record_format dspace
spelling my.uniten.dspace-342152024-10-14T11:18:28Z Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction Alkawsi G. Al-amri R. Baashar Y. Ghorashi S. Alabdulkreem E. Kiong Tiong S. 57191982354 57224896623 56768090200 57219241229 55320872600 57219799117 Anomaly detection Clustering Computational power Data stream Entropy High-dimensionality IoT Anomaly detection Cluster analysis Clustering algorithms Data mining Internet of things Petroleum reservoir evaluation % reductions Anomaly detection Clustering methods Clusterings Computational power Data stream Data stream clustering High dimensionality High-dimensional data streams Reduction algorithms Entropy In 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 AUTHORS Final 2024-10-14T03:18:28Z 2024-10-14T03:18:28Z 2023 Article 10.1016/j.aej.2023.03.008 2-s2.0-85149833939 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149833939&doi=10.1016%2fj.aej.2023.03.008&partnerID=40&md5=5c75f128e66ab093f54c6a54183098d4 https://irepository.uniten.edu.my/handle/123456789/34215 70 503 513 All Open Access Gold Open Access Elsevier B.V. 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 Anomaly detection
Clustering
Computational power
Data stream
Entropy
High-dimensionality
IoT
Anomaly detection
Cluster analysis
Clustering algorithms
Data mining
Internet of things
Petroleum reservoir evaluation
% reductions
Anomaly detection
Clustering methods
Clusterings
Computational power
Data stream
Data stream clustering
High dimensionality
High-dimensional data streams
Reduction algorithms
Entropy
spellingShingle Anomaly detection
Clustering
Computational power
Data stream
Entropy
High-dimensionality
IoT
Anomaly detection
Cluster analysis
Clustering algorithms
Data mining
Internet of things
Petroleum reservoir evaluation
% reductions
Anomaly detection
Clustering methods
Clusterings
Computational power
Data stream
Data stream clustering
High dimensionality
High-dimensional data streams
Reduction algorithms
Entropy
Alkawsi G.
Al-amri R.
Baashar Y.
Ghorashi S.
Alabdulkreem E.
Kiong Tiong S.
Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
description In 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 AUTHORS
author2 57191982354
author_facet 57191982354
Alkawsi G.
Al-amri R.
Baashar Y.
Ghorashi S.
Alabdulkreem E.
Kiong Tiong S.
format Article
author Alkawsi G.
Al-amri R.
Baashar Y.
Ghorashi S.
Alabdulkreem E.
Kiong Tiong S.
author_sort Alkawsi G.
title Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
title_short Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
title_full Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
title_fullStr Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
title_full_unstemmed Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
title_sort towards lowering computational power in iot systems: clustering algorithm for high-dimensional data stream using entropy window reduction
publisher Elsevier B.V.
publishDate 2024
_version_ 1814061046336323584
score 13.209306