An Efficient Survey of Managing Big Data Analytics using Swarm Intelligence
Keywords:
Big Data Analytics, Swarm Intelligence (SI), Particle Swarm Optimization (PSO).Abstract
Within the current scenario, the big data analytics is wide within the discussion. The characteristics of massive information are high volume, high variety, and high velocity. These characteristics create massive information analysis difficult. Challenges visages are high dimensionality, dynamically ever-changing information. Swarm intelligence has a capability to resolve dynamical, huge, and multi-objective issues. Here we focus on proving that various huge data analytics problem is solved using swarm intelligence and it is applied to Hadoop design. During this paper, we use a particle swarm optimization rule to form clusters of given dataset. Several huge data analytics issues are solved using swarm intelligence technique.
References
- Sonu L al Gupta, Sofia Goel, Anurag Singh Baghel “An Approach to Handle Big Data Analytics Using Potential of SwarmIntelligence”, International Conference on Computing for Sustainable Global Development, IEEE 2016.
- Shi Cheng, Yuhui Shi, Quande Qin, and RuibinBai “Swarm Intelligence in Big data Analytics”,14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings, pp 417-426.
- Lim Kian Sheng et al. “Multi-Objective Particle Swarm Optimization Algorithms – A Leader Selection Overview”, IEEE, DOI 10.5013/IJSSST.a.15.04.02.
- Ajith Abraham et al. “Swarm Intelligence Algorithm for Data Clustering“, In: Soft computing for Knowledge discovery and data mining, pp279-313, 2008.
- Bing Xue and Will N. Browne “Particle Swarm Optimization for Feature Selection in Classification: A multi-objective approach,” IEEE Transaction on Cybernetics, vol.43, no.6, pp.1656-1671, 2013.
- Change Li and Shengxiang Yang “A Clustering Particle Swarm Optimizer for Dynamic Optimization”, IEEE,978-1-4244-2959-2/09/$25.00-c2009.
- Dr.M.Seetha, G. Malini Devi, Dr.K.V.N.Sunitha“An Efficient Hybrid Particle Swarm Optimization for Data Clustering”
- International Journal of Data Mining & Knowledge Management Process (IJDKP) vol.2, No.6, November 2012.
- Qing and J.Deng,” Dynamic Particle Swarm Optimization Algorithm”, based on Random Topology. The scientific world jounal.2013.
- SonuLal Gupta, Sofia Goel, Anurag Singh Baghel “An Approach to Handle Big Data Analytics Using Potential of Swarm Intelligence”, International Conference on Computing for Sustainable Global Development, IEEE 2016.
- Shi Cheng, Yuhui Shi, Quande Qin, and RuibinBai “Swarm Intelligence in Big data Analytics”,14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings, pp 417-426.
- Lim Kian Sheng et al. “Multi-Objective Particle Swarm Optimization Algorithms – A Leader Selection Overview”, IEEE, DOI 10.5013/IJSSST.a.15.04.02.
- Ajith Abraham et al. “Swarm Intelligence Algorithm for Data Clustering“, In: Soft computing for Knowledge discovery and data mining, pp279-313, 2008.
- Bing Xue and Will N. Browne “Particle Swarm Optimization for Feature Selection in Classification: A multi-objective approach,” IEEE Transaction on Cybernetics, vol.43, no.6, pp.1656-1671, 2013.
- Changhe Li and Shengxiang Yang “A Clustering Particle Swarm Optimizer for Dynamic Optimization”, IEEE,978-1-4244-2959-2/09/$25.00-c2009.
- Dr.M.Seetha, G. Malini Devi, Dr.K.V.N.Sunitha“An Efficient Hybrid Particle Swarm Optimization for Data Clustering”
- International Journal of Data Mining & Knowledge Management Process (IJDKP) vol.2, No.6, November 2012.
- Q.Ni and J.Deng,” Dynamic Particle Swarm Optimization Algorithm”, based on Random Topology. The scientific world jounal.2013.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.