Efficient Utility Based Infrequent Weighted Item-Set Mining

Association Rule Mining (ARM) is one of the most popular data mining techniques. Most of the past work is based on frequent item-set. In current years, the concentration of researchers has been focused on infrequent item-set mining. The infrequent item-set mining problem is discovering item-sets whose frequency of the data is less than or equal to maximum threshold. This paper addresses the mining of infrequent item-set. To address this issue, the IWI-support measure is defined as a weighted frequency of occurrence of an item set in the analyzed data. This Infrequent weighted item set mining discovers frequent item sets from transactional databases using only items occurrence frequency and not considering items utility. But in many real world situations, utility of item sets based upon user‘s perspective such as cost, profit or revenue is of significant importance. In our proposed system we are proposing the High Utility based Infrequent Weighted Item set mining (HUIWIM). High Utility based Infrequent Weighted Item set mining (HUIWIM) to find high utility Infrequent weighted item set based on minimum threshold values and user preferences. The proposed system is used for efficiently and effectively mine high utility infrequent weighted item set from databases and it can improve the performance of the system compared to the existing system.