Stochastic Mining of Quantitative Association Rules Using Multi Agent Systems
Discovering optimized intervals of numeric attributes in association rule mining has been recognized as an influential research problem over the last decade. There have been several stochastic optimization approaches such as evolutionary and swarm methods which try to find good intervals. One drawback of these approaches is sequential nature which requires multiple runs to find all rules. This paper presents multi agent architecture to find optimized rules simultaneously using a dynamic priority schema. The Practical Swarm Optimization (PSO) Variant is modeled and implemented in JADE framework and tested with synthetic datasets. The results confirm finding the same sequential results in parallel.