Feature Selection for Post Processing In High Dimensional Data

Abstract: This paper presents a Genetic algorithm based association rule mining in which multi fitness functions are used. Genetic algorithm is used for performing global search. This proposed algorithm generates intersecting association rules from dataset. A fitness function with parameter support is defined for generating frequent itemsets and then other parameters like confidence, lift, leverage etc are used for defining next fitness function for generating association rules. The proposed algorithm is compared with classical Apriori algorithm and also with existing Genetic algorithm for association rule mining on the basis of metrics Support Count, and comparisons are also made on different generations

Keywords: Multi-Fitness Function Genetic algorithm (MFGA), Apriori algorithm, Genetic Algorithm, Crossover Probability , Fitness function, Support count, Confidence, Lift, Leverage, Coverage.