Application of associate rules mining on CGF´s behavior modeling

In this paper, Computer Generated Forces (CGF) behavior modeling was studied from the viewpoint of associate data mining, for the large quantity of data, rules and models in its process. Because CGF behavior models data source was the combination of staticDB and dynamic data stream, the paper advanced the methods of item truncation and aim-pattern restriction. Through pretreatment, coding, searching frequent pattern, generating associate rules of the CGF behavior modeling data, then decision could be made according as these rules. Application of the two methods improves on the classical aprior algorithm, also improves efficiency of searching frequent items and credibility of CGF´s decision. Finally, the application of associate rules mining in air-combat is studied in detail. As the simulation shows, comparing with the traditional matching-rule decision, associate rule mining has higher efficiency on condition with guaranteeing reliability of decision.