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

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,...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.
مؤلفون آخرون:	57191982354
التنسيق:	مقال
منشور في:	Elsevier B.V. 2024
الموضوعات:	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
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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

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