Results 141 to 150 of about 311 (178)

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
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Stable High Utility Itemset Mining

The 23rd International Conference on Information Integration and Web Intelligence, 2021
Acquah Hackman, Yu Huang, Philippe Fournier-Viger, Vincent Tseng   +3 more
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Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds

Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008
High-utility itemset mining (HUIM) is an emerging topic in data mining. It consists of discovering high-utility itemsets (HUIs), i.e. groups of items (itemsets) that generate a high profit in transactional databases. Several algorithms have been proposed for this task.
Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong   +3 more
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PHM: Mining Periodic High-Utility Itemsets

2016
High-utility itemset mining is the task of discovering high-utility itemsets, i.e. sets of items that yield a high profit in a customer transaction database. High-utility itemsets are useful, as they provide information about profitable sets of items bought by customers to retail store managers, which can then use this information to take strategic ...
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Quang-Huy Duong, Thu-Lan Dam   +3 more
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Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining

2023
Fongue, Audrey, Lonlac, Jerry, Tsopze, Norbert   +2 more
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High-Utility Itemset Mining

2022
V. Jeevika Tharini, B.L. Shivakumar
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HMiner: Efficiently mining high utility itemsets

Expert Systems with Applications, 2017
Abstract High utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets.
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