A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service Architecture

Abstract

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance measurement and the inability to obtain neighborhood information of data. To overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model and solve it by a constrained particle swarm multi-objective optimization algorithm. While we measure the distance between data by manifold distance. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.