Association Rule Mining Using Particle Swarm Optimization

Data mining is the process of discovering new relevant information in terms of patterns from large amount of data. Association rule mining is one of very important data mining techniques. Swarm optimization is a new subfield of artificial intelligence which studies the cooperative performance of simple agents. In this paper, proposed a new efficient algorithm for exploring high-class association rules by particle swarm optimization (PSO) algorithm. The proposed method mine interesting and understandable association rules without using the minimum support and the minimum confidence thresholds in only single scan. To prove the practical significance of the approach, this approach is implemented on Microsoft Visual Studio 4.0. Experimental evaluation shows the efficiency of proposed algorithm in terms of computation time.