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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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Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
Applied Sciences, 2021 When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets.
Yalong Zhang +4 more
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Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 2016 AbstrakAlgoritma yang umum digunakan dalam proses pencarian frequent itemset (data yang paling sering muncul) adalah Apriori. Tetapi Algoritma Apriori mempunyai memiliki kekurangan yaitu membutuhkan waktu yang lama dalam proses pencarian frequent itemset.
Wirdah Choiriah
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The MapReduce Model on Cascading Platform for Frequent Itemset Mining
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2018 The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models.
Nur Rokhman, Amelia Nursanti
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A Bitmap Approach for Mining Erasable Itemsets
IEEE Access, 2021 Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of ...
Tzung-Pei Hong +4 more
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Extracting Functional Modules from Biological Pathways [PDF]
, 2007 It has been proposed that functional modules are the fundamental units of cellular function. Methods to identify these modules have thus far relied on gene expression data or protein-protein interaction (PPI) data, but have a few limitations.
Sihai D. Zhao, Yong Li
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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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A Model-Based Frequency Constraint for Mining Associations from Transaction Data
, 2006 Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance.
Hahsler, Michael
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Frequent regular itemset mining [PDF]
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010 Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying
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