Description

Data mining is the process of mining new non trivial and potentially valuable information from large data basis. Data mining has been used in the analysis of customer transaction in retail research where it is termed as market basket analysis. Earlier data mining methods concentrated more on the correlation between the items that occurs more frequent in the transaction. In frequent itemset mining they do not consider the utility or importance of an item. The limitations of frequent items at mining led to an emerging area called utility mining. In utility items at mining the usefulness or profit of an item is considered. The term utility means the importance or profit of an item in a transaction. The main objective of high utility items at mining is to find the item set having utility values above the given threshold. In this paper we present a literature study on various mining algorithms. Traditional association rule mining based on the support-confidence framework provides the objective measure of the rules that are of interest to users. Although several studies have been carried out, current methods may present too many high utility itemsets for users, which degrades the performance of the mining task in terms of execution and memory efficiency. The system propose a novel framework in this paper for mining closed high utility itemsets, which serves as a compact and lossless representation of high utility itemsets. The system present an efficient algorithm called CHUD (Closed High Utility itemset Discovery) for mining closed high utility itemsets.