Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applyingassociation rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Borderalgorithm etc., which take too much computer time to compute all the frequent itemsets. By usingGenetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in thediscovery of frequent itemsets is that they perform global search and its time complexity is lesscompared to other algorithms as the genetic algorithm is based on the greedy approach. Themain aim of this paper is to find all the frequent itemsets from given data sets using geneticalgorithm