Results 161 to 170 of about 2,316 (204)

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
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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
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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
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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
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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

Up Approach: Mining High Utility Itemsets

International Journal of Computer & Orgnanization Trends, 2014
Now a days high utility item sets specially from large transaction databases is required task to process many day to day operations in quick time. In many relevant algorithms presented those are surface the problem of generating large number of candidate item set and thus degrades the mining performance in terms of execution time and space.
Arshia Sultana, Mrs E.Krishnaveni Reddy
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Binary partition for itemsets expansion in mining high utility itemsets

Intelligent Data Analysis, 2016
High utility itemset mining has recently emerged to address the limitations of frequent itemset mining. It entails relevance measures to reflect both statistical significance and user expectations. Whether breadth-first or depth-first search algorithms are employed, most methods generate new candidates by 1-extension of existing itemsets (i.e., by ...
Song, Wei, Wang, Chunhua, Li, Jinhong
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Targeted High-Utility Itemset Querying

IEEE Transactions on Artificial Intelligence, 2021
Jinbao Miao, Shicheng Wan, Wensheng Gan, Jiayi Sun, Jiahui Chen   +4 more
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Efficient high utility itemset mining using buffered utility-lists

Applied Intelligence, 2017
Discovering high utility itemsets in transaction databases is a key task for studying the behavior of customers. It consists of finding groups of items bought together that yield a high profit. Several algorithms have been proposed to mine high utility itemsets using various approaches and more or less complex data structures. Among existing algorithms,
Quang-Huy Duong, Philippe Fournier-Viger, Heri Ramampiaro, Kjetil Nørvåg, Thu-Lan Dam   +4 more
openaire   +1 more source