A Framework of Multi-Agent Professional Search Engine Based on Rough Set and Data Mining
Hongjiang Wu, Qinke Peng, Yongxuan Huang · 2006
To meet the requirement, analyzing and mining on Web content based on machine learning is a major tendency of the computer science. This paper proposes a framework of multi-agent professional search engine system based on rough set and data mining. We build a multi-agent system, analyze the Web content based on rough set and data mining and enhance the learning capability of the agent based on Bayesian method. By this means, we can optimize the search strategies and improve the intelligence of search engine. Lastly, the architecture and implementation of ASE is discussed, and the performance is tested.