Results 161 to 170 of about 498 (210)
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Mining high average-utility itemsets
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009The average utility measure is adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction including the target itemset as ...
Tzung-Pei Hong +2 more
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Mining of high‐utility itemsets with negative utility
Expert Systems, 2018AbstractHigh‐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 +3 more
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Approximate high utility itemset mining in noisy environments
Knowledge-Based Systems, 2021 High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than
Yoonji Baek, Unil Yun, Heonho Kim
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A Visualizer for High Utility Itemset Mining
2014 IEEE 17th International Conference on Computational Science and Engineering, 2014Mining high utility item sets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although several studies have been carried out, current methods present the mined results in the form of textual lists for users, which ...
Wei Song 0004, Mingyuan Liu
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Mining summarization of high utility itemsets
Knowledge-Based Systems, 2015Mining interesting itemsets from transaction databases has attracted a lot of research interests for decades. In recent years, high utility itemset (HUI) has emerged as a hot topic in this field. In real applications, the bottleneck of HUI mining is not at the efficiency but at the interpretability, due to the huge number of itemsets generated by the ...
Xiong Zhang, Zhi-Hong Deng 0001
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Binary partition for itemsets expansion in mining high utility itemsets
Intelligent Data Analysis, 2016High 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 ...
Wei Song 0004, Chunhua Wang, Jinhong Li
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A survey of incremental high‐utility itemset mining
WIREs Data Mining and Knowledge Discovery, 2018Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items ...
Wensheng Gan +5 more
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An incremental mining algorithm for high utility itemsets
Expert Systems with Applications, 2012Association-rule mining, which is based on frequency values of items, is the most common topic in data mining. In real-world applications, customers may, however, buy many copies of products and each product may have different factors, such as profits and prices.
Chun-Wei Lin 0001 +2 more
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Mining Minimal High-Utility Itemsets
2016Mining 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 +4 more
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Efficient closed high-utility itemset mining
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016This paper presents a novel algorithm for discovering closed high-utility itemsets (CHUIs) efficiently. It proposes three strategies to mine CHUIs efficiently: closure jumping, forward closure checking and backward closure checking. It also relies on two new upper-bounds named local utility and sub-tree utility to prune the search space, and a Fast ...
Philippe Fournier-Viger +4 more
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