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2017 IEEE International Conference on Big Data (Big Data), 2017
Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time
Tzung-Pei Hong +4 more
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Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time
Tzung-Pei Hong +4 more
openaire +1 more source
Approximate high utility itemset mining in noisy environments
Knowledge-Based Systems, 2020High 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 +6 more
semanticscholar +1 more source
Mining high occupancy itemsets
Future Generation Computer Systems, 2020Abstract Frequent itemset mining has been extensively studied in data mining for over the last two decades because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world applications, such as the travel landscapes recommendation, where o c c u p a n
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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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Exploiting GPU and cluster parallelism in single scan frequent itemset mining
Information Sciences, 2019This paper considers discovering frequent itemsets in transactional databases and addresses the time complexity problem by using high performance computing (HPC). Three HPC versions of the Single Scan (SS) algorithm are proposed.
Y. Djenouri +3 more
semanticscholar +1 more source
Geometrically Inspired Itemset Mining
Sixth International Conference on Data Mining (ICDM'06), 2006In our geometric view, an itemset is a vector (itemvector) in the space of transactions. Linear and potentially non-linear transformations can be applied to the itemvectors before mining patterns. Aggregation functions and interestingness measures can be applied to the transformed vectors and pushed inside the mining process.
Florian Verhein, Sanjay Chawla
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Finding tendencies in streaming data using Big Data frequent itemset mining
Knowledge-Based Systems, 2019The amount of information generated in social media channels or economical/business transactions exceeds the usual bounds of static databases and is in continuous growing.
Carlos Fernandez-Basso +3 more
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Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction
Computers & security, 2019The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible.
Shaoxin Li +3 more
semanticscholar +1 more source
An efficient utility-list based high-utility itemset mining algorithm
Applied intelligence (Boston), 2022Zaihe Cheng +4 more
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A predictive GA-based model for closed high-utility itemset mining
Applied Soft Computing, 2021Chun-Wei Lin +4 more
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