High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most measurements are expected. In real time applications, such as market basket review, company decision making web administration organization results, large quantities datasets slowly evolving with new knowledge incorpora...

High-utility itemset mining (HUIM) is emerging as an important research topic in data mining. Most algorithms for HUIM can only handle precise data, however, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is ...

One of the important research area in data mining is high utility pattern mining. Discovering itemsets with high utility like profit from database is known as high utility itemset mining. There are number of existing algorithms have been work on this issue. Some of them incurs problem of generating large number of candidate itemsets. This leads to degrade the performance of mining in case of ex...

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...

We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long transactions (that is, transactions containing many items)...