Results 161 to 170 of about 530 (210)

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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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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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 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
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An incremental mining algorithm for high utility itemsets

Expert Systems with Applications, 2012
Association-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, Guo-Cheng Lan, Tzung-Pei Hong   +2 more
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Maintaining high-utility itemsets in dynamic databases

2014 International Conference on Machine Learning and Cybernetics, 2014
Utility mining is used to measure the utility values of the purchased items from transactional database. It usually considers not only the occurrence frequencies of items but also the factors of profit, cost and quantity. In the past, many algorithms were proposed to mine high-utility itemsets from a static database.
Chun-Wei Lin 0001, Wensheng Gan, Tzung-Pei Hong, Chien-Ming Chen 0001   +3 more
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Discovering high utility itemset using MapReduce

2016 3rd International Conference on Systems and Informatics (ICSAI), 2016
Based on the MapReduce framework, we propose HUIMR algorithm on discovering high utility itemset (HUI). The HUIMR algorithm consists of counting and mining two stages. For the counting stage, MapReduce is used to calculate high transaction-weighted utilization items.
Wei Song 0004, Jiapei Xu
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Efficient closed high-utility itemset mining

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016
This 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, Souleymane Zida, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng   +4 more
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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
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