Abstract: Traditional data mining techniques have focused mainly on detecting the statistical correlations between the items that are more frequent in the transaction databases. Also termed as frequent itemset mining, these techniques were based on the rationale that itemsets which appear more frequently must be of more importance to the user from the business perspective. In recent years, the research community has also been focused on the infrequent itemset mining problem, i.e., discovering itemsets whose frequency of occurrence in the analyzed data is less than or equal to a maximum threshold. This work addresses the discovery of infrequent and weighted itemsets, i.e., the infrequent weighted itemsets, from transactional weighted data sets. To address this issue, the IWI-support measure is defined as a weighted, frequency of occurrence of an itemset in the analyzed data. In particular, we focus our attention on two different IWI-support measures: (i) The IWI-support- min measure, (ii) The IWI-support-max measure. Furthermore, two algorithms that perform IWI and Minimal IWI mining efficiently. Here, we throw light upon an emerging area called Utility Mining which not only considers the weighted frequency of the itemsets but also considers the utility associated with the itemsets. The term utility refers to the importance or the usefulness of the appearance of the itemset in transactions quantified in terms like profit, sales or any other user preferences. To address this issue, in our system we are proposing the Utility based Infrequent Weighted Itemset mining (UIWIM) to find high utility Infrequent weighted itemset based on minimum threshold values and user preferences

Keywords: Association rule, Utility mining, clustering.