Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user’s interest or preference. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. On-shelf utility mining considers on-shelf time period of item and gets the accurate utility values of it...

When companies seek for the combination of products which can constantly generate high profit, the association rule mining (ARM) or the utility mining will not achieve such task. ARM mines frequent itemsets without knowing the producing profit. On the other hand, the utility mining seeks high profit items but no guarantee the frequency. In this paper, we propose a novel utility-frequent mining ...

A pattern is of utility to a person if its use by that person contributes to reaching a goal. Utility based measures use the utilities of the patterns to reflect the user’s goals. In this paper, we first review utility based measures for itemset mining. Then, we present a unified framework for incorporating several utility based measures into the data mining process by defining a unified utilit...

Utility mining finds out high utility itemsets by considering both the profits and quantities of items in transactions. In this paper, a three-scan mining approach is proposed to efficiently discover high utility itemsets from transaction databases. The proposed approach utilizes an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from tr...

Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In ...

Association Rule Mining (ARM) is a well-studied technique that identifies frequent itemsets from datasets and generates association rules by assuming that all items have the same significance and frequency of occurrence without considering their utility. But in a number of real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is based...

High utility itemset (HUI) mining is a popular data mining task. It consists of discovering sets of items generating high profit in a transaction database. Several efficient algorithms have been proposed for this task. But few can handle items with negative unit profits despite that such items occurs in many real-life transaction databases. Mining HUIs in a database where items have positive an...

High utility itemset mining (HUIM) has emerged as an important research topic in data mining, with applications to retail-market data analysis, stock market prediction, and recommender systems, etc. However, there are very few empirical studies that systematically compare the performance of state-of-the-art HUIM algorithms. In this paper, we present an experimental evaluation on 10 major HUIM a...