Efficient Data Mining with Evolutionary Algorithms for Cloud Computing Application


Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.


With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of data mining is very important, this paper proposes four faster classification algorithms in comparison with each other. In this paper, A Multi-Layer perceptron (MLP) Network is trained with Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Invasive Weed Optimization (IWO) separately. The classifications are done on Wisconsin Breast Cancer (WBC) data base. At the end, to illustrate the speed and accuracy of these classifiers, they are compared with each other and two other types of Genetic algorithm classifiers (GA).


C. Jin, SP. DAI and JL. GUO, “Tutorial of artificial intelligence”, Beijing: Tsinghua University Press, 2007 (In Chinese).
M. E. GUO and C. Z. XU, “Cloud computing programming model and adaptive resource management”, CCCF, Vol. 7, No. 7, pp. 26–33, July 2011 (In Chinese).
P. Liu, “Cloud computing”, Beijing: Electronic Industry Press, 2011 (In Chinese).
K. Keahey, R. Figueiredo, J. Fortes, T. Freeman and M. Tsugawa, “Science Clouds: Early Experiences in Cloud Computing for Scientific Applications”, in Proc. of High Performance Computing and Communications, 2008.
H. Malmir, F. Farokhi and R. Sabbaghi-Nadooshan, “Optimization of Data Mining with Evolutionary Algorithms for Cloud Computing Application”, International Conference on Computer and Knowledge Engineering (ICCKE), pp. 354–358, 2013.
K. Chen and WM. Zheng, “Cloud computing: System instances and current research”, Journal of Software, Vol. 20, No. 5, pp. 1337–1348, 2009 (In Chinese).
P. Wilding, M.A. Morgan, A.E. Grygotis, M.A. Shoffner and E.F. Rosato, “Application of backpropagation neural networks to diagnosis of breast and ovarian cancer”, Cancer Letter, Vol. 77, No. 2–3, pp. 145–153, March 1994.
P. Tang and Z. Xi, “The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization”, Second Intl. Symp. on Intelligent Information Technology Application, IITA '08, Vol. 2, pp. 13–16, Dec. 2008.
C. Giannella, K. Liu, T. Olsen and H. Kargupta, “Communication efficient construction of decision trees over heterogeneously distributed data”, in Proc. of the Fourth IEEE Int. Conf. on Data Mining, pp. 67–74, 2004.
J. Wang, J. Wan, Z. Liu and P. Wang, “Data mining of mass storage based on cloud computing”, in Proc. of Ninth Int. Conf. on Grid and Cooperative Computing, 2010.
J. Ding and S.Yang,” Classification Rules Mining Model with Genetic Algorithm in Cloud Computing”, Int. Journal of Computer Applications, Vol.48, No.18, pp.24–32, June 2012.
T. Hu, H. Chen, L. Huang and X. Zhu, “A Survey of Mass Data Mining Based on Cloud computing”, Int. Conf. on Anti-Counterfeiting, Security and Identification (ASID), Taipei, pp. 1–4, Aug. 2012.
E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition”, in IEEE Congress on Evolutionary Computation (CEC), pp. 4661–4667, 2007.
J. Kennedy and R. C. Eberhart, “Particle swarm optimization” , in Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, pp.1942–1948, Nov. 1995.
P. Angeline, “Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences”, in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E.Eiben, Eds. Berlin, Germany: Springer-Verlag, pp. 601–610,1998.
J. Kennedy, “The particle swarm: Social adaptation of knowledge”, in Proc. Int. Conf. Evolutionary Computation, Indianapolis, IN, pp. 303–308, Apr. 1997.
J. Kennedy, “Methods of agreement: Inference among the eleMentals”, in Proc. 1998 IEEE Int. Symp. Intelligent Control, pp.883–887, Sept. 1998.
Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm adaptation”, in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Germany: Springer-Verlag, pp. 591–600, 1997.
M. Clerc and J. Kennedy, “The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58–73, Februrary 2002.
R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol. 11, No. 4, pp. 341–359, 1997.
J. Ionen, J. K. Kamarainen and J. Lampinen, “Differential evolution training algorithm for feed-forward neural networks”, Neural Process Letters, Vol. 17, No. 1, pp. 93–105, 2003.
R. Storn, “Designing nonstandard filters with differential evolution”, IEEE Signal Processing, Vol. 22, No. 1, pp. 103–106, 2005.
R. Joshi and A. C. Sanderson, “Minimal representation multisensory fusion using differential evolution”, IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 29, No. 1, pp. 63–76,1999.
A. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization”, Ecol. Inform., Vol. 1, No. 4, pp. 355–366, Dec. 2006.
S. Karimkashi and A. A. Kisk, “Invasive Weed Optimization and its Features in Electromagnetics”, IEEE Transactions on Antennas and Propagation, Vol. 58, No. 4, pp. 1269–1278, Apr. 2010.
M.I.K.M. Safari, N.Y. Dahlan, N.S. Razli and T.K.A. Rahman, “Electricity Prices Forecasting Using ANN Hybrid with Invasive Weed Optimization (IWO)”, IEEE 3rd International Conference on System Engineering and Technology (ICSET), pp. 275–280, Aug. 2013.
A. Ghosh, A. Chowdhury, R. Giri, S. Das and A. Abraham,“A hybrid evolutionary direct search technique for solving Optimal Control problems”, 10th international Conference on Hybrid Intelligent Systems (HIS), pp. 125–130, Aug. 2010.
R. Giri, A. Chowdhury, A. Ghosh, S. Das, A. Abraham, V. Snasel, “A Modified Invasive Weed Optimization Algorithm for training of feed-forward Neural Networks”, IEEE International Conference on System Man and Cybernetics (SMC), pp. 3166–3173, Oct. 2010.
H. William, W. Wolberg, N. Street and O. L. Mangesarian, 1992, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html