Clustering Datasets in Cloud Computing Environment for User Identification
Author:
Affiliation:
1. University of Miskolc,Institute of Information Technology,Miskolc,Hungary
2. Liverpool John Moores University,Department of Computer Science,Liverpool,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9756662/9756614/09756677.pdf?arnumber=9756677
Reference13 articles.
1. Chapter 5—statistical methods for transport demand modeling;profillidis;Modeling of Transport Demand Profillidis VA Botzoris GN Eds,2019
2. A review of clustering techniques and developments
3. Evaluation metrics for unsupervised learning algorithms;palacio-niño,2019
4. Integrating Clustering and Learning for Improved Workload Prediction in the Cloud
Cited by 3 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. EFection: Effectiveness Detection Technique for Clustering Cloud Workload Traces;International Journal of Computational Intelligence Systems;2024-08-05
2. Cloud Workload Categorization Using Various Data Preprocessing and Clustering Techniques;Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing;2023-12-04
3. SeQual: an unsupervised feature selection method for cloud workload traces;The Journal of Supercomputing;2023-04-14
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