Research On Parallel Optimization Algorithm Based On Association Rules

Association rule mining(ARM) is an important task in data mining with many practical applications. However,current methods for association rule mining have shown unstable performance for different database types and under-utilize the benefits of multi-core shared memory machines. Therefore,this thesis is proposed to solve these issues by presenting a novel parallel method for finding frequent patterns, the most computational intensive phase of ARM.The thesis proposed method, named ShaFEM(Frequent Elements Mining On Shared Memory Systems),combines two mining strategies,which are horizontal mining strategies and vertical mining strategies. This method applies the most appropriate one to each data subset of the database to efficiently adapt to the data characteristics and run fast on both sparse and dense databases. In addition,the method which applies lock-free design minimizes the synchronization needs and maximizes the data independence to enhance the scalability.The new structure lends itself well to dynamic job scheduling resulting in a well-balanced load on the new multi-core shared memory architectures.From the results of some experiments, the method that the thesis proposed runs faster and consumes less memory than the state-of-the-art parallel methods.