Efficient Data Mining Model Design for a Large Database in the Cloud Computing Environment

Abstract

Efficient data mining model design for a large database in the cloud computing environment is studied. For large databases efficiently mining problem, an efficient data mining model in the cloud computing environment based on improved manifold learning algorithms is proposed. The use of nonlinear manifold learning algorithms is able to reduce dimensionality of data vector feature in cloud computing environments, through characteristic extraction module to preprocess data, improved classical manifold learning algorithm is adopted to increase the distance between the data of sample spread intensive area and shorten the distance between the data of sample spread sparse area, prompting even overall distribution of sample database under cloud computing environment, so as to achieve accurate mining for efficient data in cloud computing environment. The experimental results show that the proposed method can accurately mine target data under cloud computing environments, with high efficiency and precision.