Results 101 to 110 of about 2,277 (164)

Exploring dietary behaviors among healthcare providers: based on association rule mining. [PDF]

Front Public Health
Shu T, Xie LX, Yuan ZJ, Fan XQ, Jiang MW, Liu T, Wang JJ, Xie HZ.   +7 more
europepmc   +1 more source

Acupoint Selection in Postoperative Ophthalmic Pain Management: A Data Mining Protocol. [PDF]

J Pain Res
Wang J, Yang F, Wang X, Pang F.
europepmc   +1 more source

Combating trade in illegal wood and forest products with machine learning. [PDF]

PLoS One
Datta D, Simeone JC, Meadows A, Outhwaite W, Keong Chen H, Self N, Walker L, Ramakrishnan N.   +7 more
europepmc   +1 more source

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HIGH UTILITY ITEMSETS MINING

International Journal of Information Technology & Decision Making, 2010
High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
YING LIU, JIANWEI LI, WEI-KENG LIAO, ALOK CHOUDHARY, YONG SHI   +4 more
openaire   +3 more sources

Mining high utility itemsets

Third IEEE International Conference on Data Mining, 2004
Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
null Raymond Chan, null Qiang Yang, null Yi-Dong Shen   +2 more
openaire   +1 more source

High-utility and diverse itemset mining

Applied Intelligence, 2021
High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-
Amit Verma, Siddharth Dawar, Raman Kumar, Shamkant Navathe, Vikram Goyal   +4 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
openaire   +1 more source

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
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