Genetic algorithm-based optimized association rule mining for multi-relational data

The multi relational data mining is one of the latest topics in data mining to find the relational patterns. In this paper, we have presented an algorithm for multi-relational rule mining using association rule mining and the optimization process. As a result of the association rule mining on the multirelational data, a number of relevant and irrelevant rules are generated. A rule is specified as a relation between two data points in the dataset. So, an optimization should be done on the mining algorithm in order to get the most relevant rules. We have adapted the technique of genetic algorithm in order to optimize the mined multi relational association rules. The genetic algorithm is one of the best optimization algorithm available and it suites the current problem because of its particular features such as the genetic operators crossover and mutation. The optimization of the rule is done by altering the fitness function of the genetic algorithm in relation with the multi relational data mining algorithm. The results from the experimental analysis showed that the proposed approach has better efficiency over the previous approaches. The most rules optimized is 198 under iterations 10 with a support of 60.