Web Association Rules Mining Based On Improved Genetic Algorithm

In recent decades, with the rapid development of Internet, World Wide Web has become the world's largest public data sources. In the face of numerous huge original data, how the investors utilize this data to analysis the current situation and predict the future effectively have already become great challenges which the mankind has faced. Therefore the Web data mining technology arose at the historic moment and can be developed rapidly. Recently, Web data mining has been one of hot topic both in theoretical research and applied research. Many scholars began to use genetic algorithms to solve the issue of Web data mining. However, it has some problems that using genetic algorithms to solve the topic of Web data mining.Firstly, this paper introduces the typical genetic algorithm idea and the steps. It proposes an improved genetic algorithm based on analyzing the genetic algorithm performance bottlenecks. A Web mining method of association rules based on this improved genetic algorithm was proposed. It discusses and analyses the genetic algorithms in detail from coding method, fitness function, crossover operators, selection operators, mutation operators and other aspects. Secondly, in this process, it discusses the problems of early-maturing typical genetic algorithm in detail. Boltzman survival mechanism based on simulated annealing algorithm and the establishment of self-adaptive crossover probability can be used to effectively solve this situation. Matlab environment is used to compile programming for solving the model and simulating genetic algorithm search process. Research results show that the Web mining methods of association rules based on improved genetic algorithm have certain comparative advantage both in the number of association rules obtained, accuracy of the algorithm and time consuming in the algorithm.