References

Balasaraswathi, M. and B. Kalpana, 2015. Metaheuristics for mining massive datasets: A comprehensive study of PSO for classification. Adv. Nat. Appl. Sci., 9: 27-39.
Direct Link  |  

Blum, C. and A. Roli, 2003. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv., 35: 268-308.
CrossRef  |  

Fan, W. and A. Bifet, 2013. Mining big data: Current status and forecast to the future. ACM. SIGKDD. Explor. Newsl., 14: 1-5.
CrossRef  |  Direct Link  |  

Fong, S., R. Wong and A.V. Vasilakos, 2016. Accelerated PSO swarm search feature selection for data stream mining big data. IEEE. Trans. Serv. Comput., 9: 33-45.
CrossRef  |  Direct Link  |  

Gandomi, A. and M. Haider, 2015. Beyond the hype: Big data concepts, methods and analytics. Int. J. Inform. Manage., 35: 137-144.
CrossRef  |  Direct Link  |  

Kakhani, M.K., S. Kakhani and S.R. Biradar, 2013. Research issues in big data analytics. Intl. J. Appl. Innovation Eng. Manage., 2: 228-232.
Direct Link  |  

Karaboga, D. and B. Basturk, 2007. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. J. Global Optim., 39: 459-471.
CrossRef  |  Direct Link  |  

Kennedy, J., 2011. Particle Swarm Optimization. In: Encyclopedia of Machine Learning, Sammut, C. and I.W. Geoffrey (Eds.). Springer, Berlin, Germany, ISBN:978-0-387-30768-8, pp: 760-766.

Kumar, R.R. and K. Binita, 2015. Visualizing big data mining: Challenges, problems and opportunities. Intl. J. Comput. Sci. Inf. Technol., 6: 3933-3937.
Direct Link  |  

Menandas, J.J. and J.J. Joshi, 2014. Data mining with parallel processing technique for complexity reduction and characterization of big data. Glob. J. Adv. Res., 1: 69-80.
Direct Link  |  

Mirjalili, S., 2016. SCA: A sine cosine algorithm for solving optimization problems. Knowl. Based Syst., 96: 120-133.
Direct Link  |  

Najafabadi, M.M., F. Villanustre, T.M. Khoshgoftaar, N. Seliya and R. Wald et al., 2015. Deep learning applications and challenges in big data analytics. J. Big Data, 2: 1-21.
CrossRef  |  Direct Link  |  

Nesmachnow, S., 2014. An overview of metaheuristics: Accurate and efficient methods for optimisation. Intl. J. Metaheuristics, 3: 320-347.
Direct Link  |  

PARC., 2009. Innovation at Google: The physics of data. PARC, Palo Alto, California, USA. http://www.parc.com/event/936/innovation - at - google.html

Pitre, R. and V. Kolekar, 2014. A survey paper on data mining with big data. Intl. J. Innovative Res. Adv. Eng., 1: 178-180.
Direct Link  |  

Scalable, 2012. Energy-efficient data centers and clouds, 2012. Master Thesis, The Institute for Energy Efficiency, University of California, Santa Barbara, California.

Short, E., R.E. Bohn and C. Baru, 2011. How much informat ion? 2010 report on enterprise server information. Master Thesis, Global Information Industry Center, University of California, San Diego, California.

Smullen, C.W., V. Mohan, A. Nigam, S. Gurumurthi and M.R. Stan, 2011. Relaxing non-volatility for fast and energy-efficient STT-RAM caches. Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture (HPCA), February 12-16, 2011, IEEE, San Antonio, Texas, USA., ISBN:978-1-4244-9432-3, pp: 50-61.

Tang, R., S. Fong, X.S. Yang and S. Deb, 2012. Wolf search algorithm with ephemeral memory. Proceedings of the 7th International Conference on Digital Information Management, August 22-24, 2012, Macau, pp: 165-172.

Wu, X., X. Zhu, G.Q. Wu and W. Ding, 2014. Data mining with big data. IEEE Trans. Knowledge Data Eng., 26: 97-107.
CrossRef  |  Direct Link  |  

Zhou, Z.H., N.V. Chawla, Y. Jin and G.J. Williams, 2014. Big data opportunities and challenges: Discussions from data analytics perspectives (discussion forum). IEEE. Comput. Intell. Mag., 9: 62-74.
CrossRef  |  Direct Link  |