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Generally frequent itemsets are extracted from large databases by applying association rule mining (ARM) algorithms like Apriori, Partition, Pincer-Search, Incremental, and Border algorithm etc. Genetic Algorithm (GA) can also be applied to discover the frequent patterns from databases. The main advantage of using GA in the discovery of frequent patterns or itemsets is that they can perform global search and its time complexity is lesser compared to other Apriori-based algorithms as because it is based on the greedy approach. But the FP-tree algorithm is considered to be the best among the ARM algorithms, because its candidate sets generation procedure is completely different from Apriori-based algorithms. The major aim of this paper is to present a comparative study among ARM-based and GA-based approaches to data mining.