Results 171 to 180 of about 498 (210)

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

High-Utility Itemset Mining in Big Dataset

2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2019
High-utility mining (HUIM) is an extended concept from frequent itemset mining (FIM). It emphasizes the more important factors, such as profits or the weight of an itemset in commercial applications. In this paper, we assume a dataset is too big to be loaded in the memory, then propose a MapReduce framework to handle this kind of situation, and try to ...
Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Wei Min   +2 more
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

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
openaire   +1 more source

Correlated High Average-Utility Itemset Mining

2020
High average-utility itemset (HAUI) mining is an advancement over high utility itemset mining, where average-utility is used instead of utility measure to discover meaningful patterns. It has been discussed in several past studies that significance of utility-based patterns can be amplified if items in the patterns are correlated.
Krishan Kumar Sethi, Dharavath Ramesh
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 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

Efficiently mining uncertain high-utility itemsets

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

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
openaire   +1 more source