Item set mining is one of the popular data mining techniques in which frequent and infrequent patterns can be mined. Currently the research focuses on infrequent pattern mining. The generating of infrequent item set is valid for the data coming from the distinct real life application background like Statistical disclosure risk evaluation from census data and Fraud detection. Infrequent Weighted Association Mining (IWAM) is one of the main areas in data mining for extracting the rare items in high dimensional datasets. This paper explains about the recent methods proposed for infrequent weighted item set mining. One method is IWI and MIWI to mine the minimal infrequent weighted item sets which uses FPtree. These methods work efficiently with real weighted data. Another method is based on clustering. This method also scales well and performance has been improved with time and space complexity.