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 ...

Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high...

This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to iden...

An emerging topic in the field of data mining is Utility Mining. The main objective of Utility Mining is to identify the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to find itemsets that have utility above a user-specified threshold. Itemset Utility Mining is an extension of Frequen...

Mining high utility itemsets has gained much significance in the recent years. When the data arrives sporadically, incremental and interactive utility mining approaches can be adopted to handle users‟ dynamic environmental needs and avoid redundancies, using previous data structures and mining results. The dependence on recommendation systems has exponentially risen since the advent of search e...

Utility mining in data mining has recently been an emerging research issue due to its practical applications. In this paper, with the concept of projection technique, we propose an efficient algorithm for finding high utility itemsets in databases. In particular, an improved upper-bound strategy in the proposed algorithm is designed to further tighten the upper bounds of the utility values for ...

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to mo...

High-utility itemset mining is the task of finding the sets of items that yield a high utility (e.g. profit) in quantitative transaction databases. An important limitation of previous work on high-utility itemset mining is that utility is generally used as the sole criterion for assessing the interestingness of patterns. This leads to finding many itemsets that have a high profit but contain it...

An itemset in traditional utility mining only considers individual profits and quantities of items in transactions but not its itemset length. The average-utility measure, which is the total utility of an itemset divided by its number of items within it, was then proposed to reveal a better utility effect than the original utility one. However, their proposed approach was based on the principle...

Ling Guo. Randomization Based Privacy Preserving Categorical Data Analysis. Under the direction of Dr. Xintao Wu The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today’s society. Since data mining often involves sensitive information of individuals, the pu...