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Finding the True Frequent Itemsets [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $θ$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understanding of the underlying process that ...
Riondato, Matteo, VANDIN, FABIO
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A primer to frequent itemset mining for bioinformatics. [PDF]
Brief Bioinform, 2015 : Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur.
Naulaerts S +6 more
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Knowledge Engineering and Data Science, 2020 Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset.
Adie Wahyudi Oktavia Gama +1 more
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Condensed representation of frequent itemsets [PDF]
Proceedings of the 18th International Database Engineering & Applications Symposium on - IDEAS '14, 2014 One of the major problems in pattern mining is still the problem of pattern explosion, i.e., the large amounts of patterns produced by the mining algorithms when analyzing a database with a predefined minimum support threshold. The approach we take to overcome this problem aims for automatically inferring variables from the patterns found, in order to ...
Daniel Serrano, Cláudia Antunes
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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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arules - A Computational Environment for Mining Association Rules and Frequent Item Sets [PDF]
, 2005 Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün +6 more
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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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Maximal Frequent Itemset Mining Algorithm Based on Nodeset [PDF]
Jisuanji gongcheng, 2016 The major performance bottlenecks of most maximal frequent itemset mining algorithms based on FP-Tree are caused by recursively traversing and constructing conditional FP-Trees and superset check.Therefore,this paper proposes a maximal frequent itemset ...
LIN Chen,GU Junzhong
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Implementasi Data Mining Pada Perpustakaan Untuk Penentuan Tata Letak Buku Dalam Menarik Minat Baca
Techno.Com, 2022 Perpustakaan memiliki sistem informasi untuk mempermudah manajemen sirkulasi buku. Sistem informasi biasanya hanya menghasilkan laporan harian, mingguan atau bahkan bulanan saja.
Adie Wahyudi Oktavia Gama +2 more
doaj +1 more source
Frequent Itemsets for Genomic Profiling [PDF]
, 2005 Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses.
Jeannette M. de Graaf +3 more
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