The Research Of Web Data Mining Based On Particle Swarm Optimization (PSO)

With the wide use of computer technology in the field, the rapid development of the Internet makes all kinds of information to exponential growth, GB and TB-level data, how to become a part of today’s digital society faced a very challenging problem. Data contained in the value of being more and more people are concerned about and research value of mining knowledge, became inevitable requirement. Web data mining data covered by the main structure of Web content, Web structure, Web use recorded background information, transactions, user information, query information, and more. However, these data are often of a distributed, heterogeneous nature, sparse, high-dimensional character and mass characteristics of, and therefore distributed Web data mining are increasingly concerned.Particle Swarm Optimization algorithm is achieved through competition and collaboration among particles to find global optimal solutions in the complex search spaces. It is easy to understand, easy to implement, the global search abilities, and other characteristics, much widespread attention in the field of science and engineering, has become one of the fastest-growing intelligence optimization algorithm. These characteristics are due to the algorithm so it has a larger advantage in WEB data mining. This paper mainly studies the algorithm in the application of Web data mining, introducing the fundamentals of Particle Swarm Optimization algorithms for Web data mining and analysis of its characteristics. A comprehensive overview of Particle Swarm Optimization algorithm of Web data mining principles, characteristics, parameter setting and application, analysis on inertia weight in Particle Swarm Optimization algorithm, accelerating factor setting on the basic properties of the algorithm, algorithm is given experience in the parameter settings. Finally made a number of recommendations on its future research and research direction of prospects.