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Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods
IEEE transactions on industrial electronics (1982. Print), 2018The presence of alarm floods is identified as the main reason for low efficiency of alarm systems and the leading cause of many industrial accidents. In practice, a commonly used technique to deal with alarm floods is dynamic alarm suppression, which ...
Wenkai Hu, Tongwen Chen, Sirish L. Shah
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Misleading Generalized Itemset Mining in the Cloud
2014 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2014In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided ...
BARALIS, ELENA MARIA +6 more
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Frequent Itemset Mining for Big Data
2013 IEEE International Conference on Big Data, 2013Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Sandy Moens +2 more
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Efficient evolutionary computation model of closed high-utility itemset mining
Applied intelligence (Boston), 2022Chun-Wei Lin +3 more
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Efficient high average-utility itemset mining using novel vertical weak upper-bounds
Knowledge-Based Systems, 2019Discovering high average utility itemsets (HAUIs) in a quantitative database is a popular data mining task, which aims at identifying sets of products (items) purchased together that have a high importance or yield a high profit. However, a key challenge
Tin C. Truong +4 more
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Knowledge Compilation for Itemset Mining
2010We present a novel approach to itemset mining whereby the set of all itemsets are compiled into a compact form, closely related to binary decision diagrams. While there were previous attempts to utilize decision diagrams for storing the set of frequent itemsets this is the first approach that does not rely on backtrack search to generate such a set ...
Hadrien Cambazard +2 more
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2009 International Conference on Machine Learning and Cybernetics, 2009
In this paper, we introduce a new kind of mining problem — - mining erasable itemsets, which is de-rived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem.
null Zhi-Hong Deng +3 more
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In this paper, we introduce a new kind of mining problem — - mining erasable itemsets, which is de-rived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem.
null Zhi-Hong Deng +3 more
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FHUQI-Miner: Fast high utility quantitative itemset mining
Applied intelligence (Boston), 2021M. Nouioua +4 more
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On a visual frequent itemset mining
2009 Fourth International Conference on Digital Information Management, 2009Given a large, dense transaction database, generating interesting frequent patterns in a user friendly manner remains as an important issue in data mining. It is because the minimum support, the most popular statistical significance measurement, is not capable of reflecting the domain user's interest. This paper presents visual frequent itemset mining (
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Mining Frequent and Homogeneous Closed Itemsets
2016It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Inès Hilali +4 more
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