The Application Study Of Multi-Agent And The Association Rule Mining

Association rules mining is one of the most mature technology in data mining. It has been applied widely in business management, government office, scientific research and engineering development. Because the processing datasets are usually large- scale and heterogeneous ones, and considering the security and fault tolerance etc, a method which can mine interest association rules from the database intellectually, effectively, safely is needed urgently. Because of the high intelligence, effectiveness of constructing distributing system, and powerful reusable features of the Multi-Agent technology which is the top technology of distributed artificial intelligent, it provides strong support for association rule mining etc. The technology of association rule mining and the Multi-Agent are described in this paper. A system of ARSM based on association rule mining and Multi-Agent is designed, and it is applied to the action recommendation system of Web visiting. The main work is stated as follows:Firstly, using the improved constructing method of FP-Tree (Frequent Pattern tree) and the mining method of the frequent k-item set based on bit objects in BFP-Miner(Bit Frequent Pattern Miner) algorithm that has been proposed, the frequent k+1-item set that includes the frequent k-item set is generated, then the strong association rules is obtained according to the min confidence. In the system of ARSM, the method that generates the strong association rules is applied to mine the Web log to recommend action for Web visitor.Secondly, the system ARSM has been designed through analyzing the task of it. The architecture of ARSM is composed of four parts which are UserAgent,DProAgent,ARAgent and Action Log Data Base. ARAgent manages the three Agents which are DatabaseProcessAgent,BFPTreeMinerAgent and ARMAgent. In order to realize the function of each Agent, the model and the architecture of the Agent have been designed, the working process and control algorithm is also introduced. The skills of every Agent are defined. In the end, a manage framework of ARSM based on Multi-Agent is provided by analyzing the communication among Agents.Finally, by pre-proceeding the anonymous Web data that comes from the Web of Microsoft, ARSM is applied to the Web visiting to recommend action for the user. The recommend process is implemented and the skills of Agent is realized. In the end, the experimental result of the system is displayed to demonstrate its effectiveness.