Apriori vs Genetic algorithms for Identifying Frequent Item Sets

The main Objective of this paper is to mine the data from the database for set of transactions. By taking the set of itemsets as input and find the frequency of each item. We can divide the items into set of frequent items and infrequent items based on the minimum support count and then remove all infrequent itemsets in the pruning part. Then we extract the frequent items to database and place frequent items to be available to the customers to use the items frequently. In general frequent item sets are generated from large data sets by applying association rule mining algorithms like Apriori, partition algorithms etc., which take too much computer time to compute all the frequent itemsets. By using Genetic algorithm (GA) we can improve scenario. Genetic Algorithm is stochastic search algorithm modelled on the process of natural selection, works in an iteration manner by generating new populations of strings from old ones. Genetic Algorithm represents an intelligent exploitation of a random search used to solve optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space.Genetic Algorithm are one of the best ways to solve a problem for which little is known. The main aim of this project is to find all the frequent item sets from given non binary datasets using genetic algorithm.