Results 131 to 140 of about 311 (178)

Discovering high utility itemset using MapReduce

2016 3rd International Conference on Systems and Informatics (ICSAI), 2016
Based on the MapReduce framework, we propose HUIMR algorithm on discovering high utility itemset (HUI). The HUIMR algorithm consists of counting and mining two stages. For the counting stage, MapReduce is used to calculate high transaction-weighted utilization items.
Wei Song, Jiapei Xu
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

Towards Efficient Discovery of Target High Utility Itemsets

2022 IEEE International Conference on Data Mining Workshops (ICDMW), 2022
Finding High Utility Itemsets (HUls) in databases is crucial for identifying items that are of high importance (like profit) for decision-making. However, current High Utility Itemset Mining (HUIM) algorithms often ignore the interest or target of users in favor of effectively identifying categories of HUls using various measures and constraints.
Vincent Mwintieru Nofong, Priscilla Owiredu Okai, Hamidu Abdel-Fatao, Selasi Kwashie, Michael Bewong, John Wondoh   +5 more
openaire   +2 more sources

Efficient closed high-utility itemset mining

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016
This paper presents a novel algorithm for discovering closed high-utility itemsets (CHUIs) efficiently. It proposes three strategies to mine CHUIs efficiently: closure jumping, forward closure checking and backward closure checking. It also relies on two new upper-bounds named local utility and sub-tree utility to prune the search space, and a Fast ...
Philippe Fournier-Viger, Souleymane Zida, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng   +4 more
openaire   +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

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 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

Vertical mining for high utility itemsets

2012 IEEE International Conference on Granular Computing, 2012
Recently, high utility itemsets mining becomes one of the most important research issues in data mining due to its ability to consider different profit values for every item. In the past studies, most algorithms generate high utility itemsets from a set of transactions in horizontal data format.
Wei Song, Yu Liu, Jinhong Li
openaire   +1 more source

Mining of high‐utility itemsets with negative utility

Expert Systems, 2018
AbstractHigh‐utility itemset (HUI) mining is an important tasks during data mining. Recently, many algorithms have been proposed to discover HUIs. Most of the algorithms work only for itemsets with positive utility values. However, in the real world, items are found with both positive and negative utility values.
Kuldeep Singh, Harish Kumar Shakya, Abhimanyu Singh, Bhaskar Biswas   +3 more
openaire   +1 more source

Efficiently mining uncertain high-utility itemsets

Soft Computing, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lin, Jerry Chun-Wei, Gan, Wensheng, Fournier-Viger, Philippe, Hong, Tzung-Pei, Tseng, Vincent S.   +4 more
openaire   +2 more sources