Item set mining is one of the famous data mining method in which frequent and infrequent items can be mined. Now a days, the research society has focused on the problem of infrequent itemset mining, i.e., find out the item sets which has frequency of occurrence in transactional data base is less than or equal to a maximum threshold. Discovering rare item set is more interesting than mining frequent ones. The existing system deal with the issue of discovering infrequent weighted item sets. Infrequent Weighted Itemset Miner (IWI Miner) and Minimal Infrequent Weighted Itemset Miner (MIWI Miner) algorithms are introduced for efficient IWI and Minimal IWI mining. In many real world situations, utility of item sets depends on user‘s perspective such as cost, profit or revenue which are major significance. The existing Infrequent weighted item set mining algorithms are used to find out infrequent item sets from weighted transactional database, it does not compute utility of items. So in the proposed system introduced low utility based Infrequent Weighted Itemset mining (LUIWIM) algorithm. The proposed system is used for effectively mine the low utility infrequent weighted item set according to the profit, sale, etc. of items and it can improve the performance of the system compared to the existing system.

Data mining, infrequent item set, utility item

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