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Third IEEE International Conference on Data Mining, 2004
Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +2 more
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Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +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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High average-utility itemsets mining: a survey
Applied Intelligence, 2021HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-utility measure is used to obtain the utility of itemsets.
Kuldeep Singh 0003 +2 more
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Vertical mining for high utility itemsets
2012 IEEE International Conference on Granular Computing, 2012Recently, 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, 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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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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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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Mining Local High Utility Itemsets
2018High 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 +4 more
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