---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The frequent item set mining is one of the popular data mining techniques and it can be used in many data mining fields for finding highly correlated item sets. An infrequent item set mining finds rarely occurring item sets in the database. The proposed system uses clustering or logical grouping concepts for finding infrequent weighted item sets. The algorithm which is used in the proposed system, works well with real-time databases and is highly scalable which is suited for real-time applications. Incessant weighted item sets to associate frequently holding an information in which item sets may weight distinctively. The paper handles the issue of running across an extraordinary and weighted itemsets, i.e., An Infrequent Weighted Item set (IWI) mining. In this paper two novel quality measures are proposed to test the IWI (Infrequent Weighted Itemset) mining procedure. The two calculations that perform IWI and Neglectable IWI mining efficiently, which is determined by the proposed measures, are displayed. Test outcomes show the efficiency and adequacy of the proposed methodology.