Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction-Reference-Cited by-同舟云学术

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

Published:2023-05 Issue: Volume:70 Page:503-513
ISSN:1110-0168
Container-title:Alexandria Engineering Journal
language:en
Short-container-title:Alexandria Engineering Journal

Author:

Alkawsi Gamal,Al-amri Redhwan,Baashar Yahia,Ghorashi Sara,Alabdulkreem Eatedal,Kiong Tiong Sieh

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Elsevier BV

Subject

General Engineering

Reference27 articles.

1. Designing a streaming algorithm for outlier detection in data mining—an incremental approach;Yu;Sensors,2020

2. Anomaly detection for IoT time-series data: a survey;Cook;IEEE Internet Things J.,2020

3. DeepAnT: a deep learning approach for unsupervised anomaly detection in time series;Munir;IEEE Access,2019

4. Optimizing data stream representation: an extensive survey on stream clustering algorithms;Carnein;Bus. Inf. Syst. Eng.,2019

5. Evolving clustering algorithm based on mixture of typicalities for stream data mining;Maia;Future Gener. Comput. Syst.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments;IoT;2024-03-05

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