Research And Implementation Of Weighted Assosiation Rules Mining On Ant Colony Algorithm

Ant colony algorithm is one of swarm intelligence optimization algorithms,which is inspired by research on the behavior of ant colonies in real world that they are capable of finding the shortest path between food source and their nest. It has been successfully applied in many complicated optimization problem such as TSP, assignment problem, scheduling problem, set covering problem. Because of its positive feedback, parallel, distribution, self-organization and so on, it can gradually build a feasible solution according to different heuristic information inherent problems. Considering above, the ant colony algorithm research shows advantages in solving complex optimization problem, and it has been proved that is a very promising method.Association rule mining is an important research direction in Data mining, and association rule mining algorithm has received considerable attention and study. In reality, the importance of each item are different, while the traditional association rule mining algorithm set the same level of importance of each item, for example, decision makers tend to give priority to the items with higher profit, but ignored less profitable items, therefore, the weighted association rule mining is more meaningful in analyzing the data.Considering ant colony algorithm for solving complex optimization problems in the superiority and the weighted association rule mining of data in the necessity. In this paper, we propose the weighted association rule mining method based on ant colony algorithm.Compared with Apirori algorithm and weighted association rule mining methods,Experimental results show that the method can quickly and efficiently mine the important association rules.