Discovering interesting rules from biological data using parallel genetic algorithm

In this paper, a parallel genetic based association rule mining method is proposed to discover interesting rules from a large biological database. Apriori algorithms and its variants for association rule mining rely on two user specified threshold parameters such as minimum support and minimum confidence which is obviously an issue to be resolved. In addition, there are other issues like large search space and local optimality attracts many researchers to use heuristic mechanism. In the presence of large biological databases and with an aim to circumvent these problems, genetic algorithm may be taken as a suitable tool, but its computational cost is the main bottle-neck. Therefore, we choose parallel genetic algorithms to get relief from the pain of computational cost. The experimental result is promising and encouraging to do further research especially in the domain of biological science.