Optimization of data mining with evolutionary algorithms for cloud computing application

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 two faster classification algorithms in comparison with the others. In this paper, A Multi-Layer perceptron (MLP) Network is trained with Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) 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 two kinds of Genetic algorithm classifiers (GA).

[1]  Kun Liu,et al.  Communication efficient construction of decision trees over heterogeneously distributed data , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[2]  Zheng Wei,et al.  Cloud Computing:System Instances and Current Research , 2009 .

[3]  Lu Huang,et al.  A survey of mass data mining based on cloud-computing , 2012, Anti-counterfeiting, Security, and Identification.

[4]  Mann A. Shoffner,et al.  Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. , 1994, Cancer letters.

[5]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Zhaocai Xi,et al.  The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[8]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[9]  Kang Chen,et al.  Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .

[10]  Renato Figueiredo,et al.  Science Clouds: Early Experiences in Cloud Computing for Scientific Applications , 2008 .

[11]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[12]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[13]  Peng Wang,et al.  Data Mining of Mass Storage Based on Cloud Computing , 2010, 2010 Ninth International Conference on Grid and Cloud Computing.

[14]  Shanlin Yang,et al.  Classification Rules Mining Model with Genetic Algorithm in Cloud Computing , 2012 .

[15]  James Kennedy.  Methods of agreement: inference among the EleMentals , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.