Graph Based Approaches to Generate Frequent Itemsets

Association Rule Mining among Frequent Items has been widely studied in Data Mining. Many researchers have improved the algorithm for generation of all the Frequent Itemsets. Frequent Itemset mining plays an essential role in Data Mining. Various algorithms have been proposed to generate all large frequent itemsets from a large amount of transaction data using graphs. In this paper we generally review and compare the most important graph based algorithms with each other. Results shows that each algorithm based on its applied strategy has some advantages and some disadvantages. However compress and mine algorithm is more effective and takes less time and space.