A Heuristic Approach for Fast Mining Association Rules in Transportation System

This paper proposes a heuristic algorithm for fast mining association rules by multidimensional scaling (MDS). It takes the similarity measurements as the MDS proximities and develops a practical MDS model to generate decentralized configuration of points that represent the stops on vehicle routes. This algorithm extends the SMACOF algorithm by the steps of grouping and join. The experiments show that the novel algorithm has much higher efficiency than the Apriori algorithm especially when mining association rules of long patterns in transportation system.