Multi-objective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations

This study presents a fast and scalable multi-objective association rule mining technique using genetic algorithm from large database. The objective functions such as confidence factor, comprehensibility and interestingness can be thought of as different objectives of our association rule-mining problem and is treated as the basic input to the genetic algorithm. The outcomes of our algorithm are the set of non-dominated solutions. However, in data mining the quantity of data is growing rapidly both in size and dimensions. Furthermore, the multi-objective genetic algorithm (MOGA) tends to be slow in comparison with most classical rule mining methods. Hence, to overcome these difficulties we propose a fast and scalability technique using the inherent parallel processing nature of genetic algorithm and a homogeneous dedicated network of workstations (NOWs). Our algorithm exploit both data and control parallelism by distributing the data being mined and the population of individuals across all available processors. The experimental result shows that the algorithm has been found suitable for large database with an encouraging speed up.