Results 161 to 170 of about 15,856,028 (211)

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 0004, Yu Liu, Jinhong Li
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Mining high average-utility itemsets

2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
The 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, Cho-Han Lee, Shyue-Liang Wang   +2 more
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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 0003, Harish Kumar Shakya, Abhimanyu Singh, Bhaskar Biswas   +3 more
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A Visualizer for High Utility Itemset Mining

2014 IEEE 17th International Conference on Computational Science and Engineering, 2014
Mining 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, 2015
Mining 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, 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 ...
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, 2018
Traditional 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, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Tzung-Pei Hong, Hamido Fujita   +5 more
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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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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
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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