efficient 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 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).