Results 171 to 180 of about 905 (211)

Efficient Incremental High Utility Itemset Mining

Proceedings of the ASE BigData & SocialInformatics 2015, 2015
High-utility itemset mining (HUIM) in transaction databases is an important data mining task with wide applications. However, most HUIM algorithms assume the unrealistic assumption that databases are static. To address this issue, algorithms have been designed to maintain high-utility itemsets in dynamic databases. However, these incremental algorithms
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Ted Gueniche, Prashant Barhate   +3 more
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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, José María Luna, Sebastián Ventura   +4 more
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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

Efficiently mining uncertain high-utility itemsets

Soft Computing, 2016
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Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, Vincent S. Tseng   +4 more
openaire   +2 more sources

A Novel Algorithm for Mining High Utility Itemsets

2009 First Asian Conference on Intelligent Information and Database Systems, 2009
The utility based itemset mining approach has been discussed widely in recent years. There are many algorithms mining high utility itemsets by pruning candidates based on estimated utility values, and based on transaction-weighted utilization values. These algorithms aim to reduce search space.
Bac Le, Huy Nguyen, Tung Anh Cao, Bay Vo
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 0003, Harish Kumar Shakya, Abhimanyu Singh, Bhaskar Biswas   +3 more
openaire   +1 more source

Review on High Utility Itemset Mining Algorithms

Asian Journal of Research in Social Sciences and Humanities, 2016
Finding interesting patterns in the database is an important research area in the field of data mining. Association Rule Mining (ARM) finds the items that go together. It finds out the association between items. Frequent Itemset Mining (FIM) finds out the itemset that occur frequently in the database.
V. Kavitha, B. G. Geetha
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Mining of High-Utility Itemsets by ACO Algorithm

Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016, 2016
High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the quantity factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products.
Jimmy Ming-Thai Wu, Justin Zhan, Jerry Chun-Wei Lin   +2 more
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Mining high-utility itemsets with irregular occurrence

2017 9th International Conference on Knowledge and Smart Technology (KST), 2017
High-utility itemsets mining (HUIM) is proposed to discover itemsets giving high utilities (such as high profit, low cost/risk and other factors). This can help to extract hidden-knowledge from buying behavior of customers. However, HUIM may not sufficiently give hidden-knowledge and observe occurrence behavior of itemsets in some applications, since ...
Supachai Laoviboon, Komate Amphawan
openaire   +1 more source

Stable High Utility Itemset Mining

The 23rd International Conference on Information Integration and Web Intelligence, 2021
Acquah Hackman, Yu Huang 0018, Philippe Fournier-Viger, Vincent S. Tseng   +3 more
openaire   +1 more source