Traditional methods of association rule mining consider the appearance of an item in a transaction, whether or not it is purchased, as a binary variable. However, customers may purchase more than one of the same item, and the unit cost may vary among items. Utility mining, a generalized form of the share mining model, attempts to overcome this problem. Since the Apriori pruning strategy cannot ...

High-utility pattern mining is an important data mining task having wide applications. It consists of discovering patterns generating a high profit in databases. Recently, the task of high-utility sequential pattern mining has emerged to discover patterns generating a high profit in sequences of customer transactions. However, a well-known limitation of sequential patterns is that they do not p...

Utility mining developed to address the limitation of frequent itemset mining by introducing interestingness measures that satisfies both the statistical significance and the user’s expectation. Existing high utility itemsets mining algorithms two steps: first, generate a large number of candidate itemsets and second, identify high utility itemsets from the candidates by an additional scan of t...

Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets wi...

Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset Mining, which discovers itemsets that occur frequently (i.e. with occurrence count larger than a user given value). The problem of finding High Utility Itemsets is challenging, because the anti-monotone property so use...

Utility mining is to find the itemsets in a transaction database with high utility values like profits. Although a number of algorithms on high utility mining have been proposed, they did not reflect the fuzzy degree of quantity and profit level for mined high utility itemsets, which are essential for decision making in various applications like stock control and sales analysis. In this paper, ...

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. It is an extension of the frequent pattern mining. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets ...

--The utility of an itemset represents its importance, which can be measured in terms of weight, value, quantity or other information depending on the user specification. High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different i...