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Finding interesting patterns in the database is an important research area in the field of data mining. Association Rule Mining (ARM) finds the items that go together. It finds out the association between items. Frequent Itemset Mining (FIM) finds out the itemset that occur frequently in the database. But this approach misses out the profit and the quantity of item purchased. This is addressed in High Utility Itemset Mining (HUIM). HUIM find the profit generating itemset in the database. Many algorithms have been proposed in this field in the recent years. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.