Mining High Utility Itemsets–A Recent Survey

Association rule mining (ARM) plays a vital role in data mining. It aims at searching for interesting pattern among items in a dense dataset or database and discovers association rules among the large number of itemsets. The importance of ARM is increasing with the demand of finding frequent patterns from large data sources. Researchers developed a lot of algorithms and techniques for generating association rules. Since the traditional ARM approaches consider the utility of the items by its presence in the transaction set. An emerging topic in the field of data mining called Utility Mining has evolved. Since the frequency of itemset is not sufficient to reflect the actual utility of an itemset. The main objective of Utility Mining is to identify the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to find itemsets that have utility above a user-specified threshold. Mining Itemset Utility is an extension of Frequent Itemset mining, which discovers itemsets that occur frequently. Several researches about itemset utility mining were proposed. Here, a literature survey of various algorithms for high utility itemset mining has been presented.