Results 21 to 30 of about 794 (166)
IEEE Access, 2021 One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa.
Wannasiri Thurachon, Worapoj Kreesuradej
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Mining All Non-derivable Frequent Itemsets [PDF]
, 2002 3 ...
Calders, T., GOETHALS, Bart
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On differentially private frequent itemset mining [PDF]
Proceedings of the VLDB Endowment, 2012 We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long ...
Chen, Zeng +2 more
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Signature-based Tree for Finding Frequent Itemsets
Journal of Communications Software and Systems, 2023 The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure.
Mohamed El Hadi Benelhadj +2 more
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Hiding co-occurring frequent itemsets [PDF]
Proceedings of the 2009 EDBT/ICDT Workshops, 2009 Knowledge hiding, hiding rules/patterns that are inferable from published data and attributed sensitive, is extensively studied in the literature in the context of frequent itemsets and association rules mining from transactional data. The research in this thread is focused mainly on developing sophisticated methods that achieve less distortion in data
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An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data
Data, 2022 Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find
Mohamed Reda Al-Bana +2 more
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Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table [PDF]
International Journal of Computational Intelligence Systems, 2014 Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication.
Le Wang, Lin Feng, Bo Jin
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Binary image description using frequent itemsets
Journal of Big Data, 2020 In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset ...
Khalid Aznag +3 more
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Axiomatization of frequent itemsets
Theoretical Computer Science, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Calders, Toon, Paredaens, J.
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Mining Frequent Itemsets in a Stream [PDF]
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007 We study the problem of finding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint.
Calders, Toon +2 more
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