Results 141 to 150 of about 5,295 (219)

Efficient high utility itemset mining without the join operation

Information Sciences
The task of mining high-utility itemsets in a database given a minimum threshold is attracting more and more interest due to its many applications. Existing algorithms such as the vertical ones have the advantages of high scalability, e ffi ciency and ...
Yihe Yan, Xinzheng Niu, Zhiheng Zhang, Philippe Fournier-Viger, Libin Ye, Fan Min   +5 more
semanticscholar   +1 more source

Actionable Combined High Utility Itemset Mining

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered.
Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao   +3 more
openaire   +1 more source

Mining Local High Utility Itemsets

2018
High Utility Itemset Mining (HUIM) is the task of analyzing customer transactions to find the sets of items that yield a high utility (e.g. profit). A major limitation of traditional HUIM algorithms is that they do not consider that the utility of itemsets vary over time.
Philippe Fournier-Viger, Yimin Zhang, Jerry Chun-Wei Lin, Hamido Fujita, Yun Sing Koh   +4 more
openaire   +1 more source

Mining Cross-Level High Utility Itemsets

2020
Many algorithms have been proposed to find high utility itemsets (sets of items that yield a high profit) in customer transactions. Though, it is useful to analyze customer behavior, it ignores information about item categories. To consider a product taxonomy and find high utility itemsets describing relationships between items and categories, the ML ...
Philippe Fournier-Viger, Ying Wang, Jerry Chun-Wei Lin, Jose Maria Luna, Sebastian Ventura   +4 more
openaire   +1 more source

Mining top-K high utility itemsets

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012
Mining high utility itemsets from databases is an emerging topic in data mining, which refers to the discovery of itemsets with utilities higher than a user-specified minimum utility threshold min_util. Although several studies have been carried out on this topic, setting an appropriate minimum utility threshold is a difficult problem for users.
Cheng Wei Wu, Bai-En Shie, Vincent S. Tseng, Philip S. Yu   +3 more
openaire   +1 more source

A residual utility-based concept for high-utility itemset mining

Knowledge and Information Systems, 2021
Knowledge discovery in databases aims at finding useful information for decision-making. The problem of high-utility itemset mining (HUIM) has specifically garnered huge research attention, as it aims to find relevant information on patterns in a ...
Pushp Sra, S. Chand
semanticscholar   +1 more source

Mining High-Utility Irregular Itemsets

2019
High-utility itemset mining (HUIM) currently plays an important role in a wide range of applications and data mining community. Several algorithms, methods and data structures have been proposed to improve efficiency of mining for such itemsets. Besides, HUIM is extended in several aspects including the regarding of “regularity or irregularity of ...
Supachai Laoviboon, Komate Amphawan
openaire   +1 more source

Top-k high utility itemset mining: current status and future directions

Knowledge engineering review (Print)
High utility itemsets mining (HUIM) is an important sub-field of frequent itemset mining (FIM). Recently, HUIM has received much attention in the field of data mining.
Raj Kumar, Kuldeep Singh
semanticscholar   +1 more source

Mining Minimal High-Utility Itemsets

2016
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.
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng, Usef Faghihi   +4 more
openaire   +1 more source

High average-utility itemsets mining: a survey

Applied Intelligence, 2021
HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-utility measure is used to obtain the utility of itemsets.
Kuldeep Singh, Rajiv Kumar, Bhaskar Biswas   +2 more
openaire   +1 more source