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

• 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: Anomaly detection; Clustering; Computational power; Data stream; Entropy; High-dimensionality; IoT; Cluster analysis; Clustering algorithms; Data mining; Internet of things; Petroleum reservoir evaluation; % reductions; Clustering methods; Clusterings; Data stream clustering; High dimensionality; High-dimensional data streams; Reduction algorithms

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,...