Results 21 to 30 of about 659,904 (212)
On Differentially Private Frequent Itemset Mining. [PDF]
VLDB J, 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 ...
Zeng C, Naughton JF, Cai JY.
europepmc +5 more sources
Krimp: mining itemsets that compress [PDF]
Data Mining and Knowledge Discovery, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jilles Vreeken +2 more
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Weighted Association Rule Mining using Weighted Support and Significance Framework [PDF]
, 2003 We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to
Murtagh, Fionn, Tao, Feng, Farid, Mohsen
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Dominance Programming for Itemset Mining [PDF]
2013 IEEE 13th International Conference on Data Mining, 2013 Finding small sets of interesting patterns is an important challenge in pattern mining. In this paper, we argue that several well-known approaches that address this challenge are based on performing pair wise comparisons between patterns. Examples include finding closed patterns, free patterns, relevant subgroups and skyline patterns. Although progress
Benjamin Négrevergne +3 more
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Generic Itemset Mining Based on Reinforcement Learning
IEEE Access, 2022 One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets.
Kazuma Fujioka, Kimiaki Shirahama
doaj +1 more source
Non-derivable itemset mining [PDF]
Data Mining and Knowledge Discovery, 2007 All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset.
Toon Calders, Bart Goethals
openaire +4 more sources
Proposed Algorithm for Extracting Association Rule Depend on Closed Frequent Itemset (EACFI) [PDF]
Engineering and Technology Journal, 2011 Association rules are important one of data mining activities. All algorithms of association rule mining consist of finding frequency of itemsets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-itemsets to ...
Emad k. Jbbar, Yaser Munther
doaj +1 more source
A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
Applied Sciences, 2022 Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years.
Tzung-Pei Hong +5 more
doaj +1 more source
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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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