Earticle

Along with the rapid advancement of Internet technology and machine learning science, the data mining techniques have been widely applied on the web page information pattern analysis issues. To enhance the traditional mining algorithms theoretically and numerically, we propose the novel deep web data mining algorithm based on multi-agent information system and collaborative correlation rule in this manuscript. Firstly, we review the latest web mining methodologies to serve as the comparison objects. Then, we introduce the revised agent based algorithm. MAS consists of more than one agent, MAS using parallel distributed processing technology and modular design thought and the complex system is divided into relatively independent agent subsystem. Later, we combine the AdaBoost method to propose the collaborative correlation rule. As the combination, we use the mentioned two techniques to form the optimized and enhanced deep web data mining algorithm with the implementation of programming languages. The experimental result proves the feasibility of our approach and compared with other contemporary state-of-the-art algorithms, our method outperforms and achieves better accuracy with low time-consuming.