Open Access
| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03031 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203031 | |
| Published online | 29 July 2020 | |
- Georgios P. Papamichail, Dimitrios P. Papamichail, “The k-means range algorithm for personalized data clustering in e-commerce”, European Journal of Operational Research, ELSEVIER,2007. [Google Scholar]
- Ibrahim Aljarah and** Simone A. Ludwig ,”Parallel Particle Swarm optimization Clustering Algorithm”. Fourth World Congress on Nature and Biologically Inspired Computing,IEEE, 2012 [Google Scholar]
- Gang Cheng, Chao Lv, Shi Yan, Li Xu, “A Novel Hybrid Optimization Algorithm Combined with BBO and PSO”,in Chinese Control and Decision Conference ,IEEE, 2016. [Google Scholar]
- Joseph Issa, “Performance characteristics and analysis for Hadoop k-Means iteration,’Issa Journal of cloud computing:Advances, system, SpringerOpen, 2016. [Google Scholar]
- JiaHao Hunag, Tiejun wang,, Xuhui Zhu, Min Wei, Tao Wu, Xi Wu, Min Huang, ”A Parallel Optimization of Fast Algorithm of convolution Neural network on CPU”, 10’thInternational Conference on Measuring Technology and Mechatronics Automation,IEEE, 2018. [Google Scholar]
- R.S.M. Lakshmi Patibandle, N. Veeranjaneyulu, ‘Performance Analysis ofPartition and Evolution Clusterin Methos on Various Cluster Open, 2018. [Google Scholar]
- Mr.Likhesh Kolhe, Dr. Vaishali Khairnar, Dr. Ashok kumar jetwa; ’ Performance M easures Analysis Using Parallel Clustered Optimization techniques For I-Commerce,”8th International Conference on cloud computing, data science Enginnering,IEEE,2018. [Google Scholar]
- https;//drive.google.com/open?id=1rZsvBXPhlNeBMZRqKINhpGUJrYTqNqc [Google Scholar]
- https://drive.google.com/open?id=1pkVTQCd94EA6NGuw_WRQrOjt52b37PVu. [Google Scholar]
- https://drive.google.com/open?id=1jBydlmonXOmr1SrhJWvc6CZgsqrnR [Google Scholar]
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