Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm. Keywords- Genetic Algorithm (GA), Association Rule, Frequent itemset, Support, Confidence, Data Mining.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Soumadip Ghosh, Sushanta Biswas, Debasree Sarkar, Partha Pratim Sarkar
• Keywords: Algorithm, CORR 2010, Education, Frequent Itemset, Genetic Algorithm