Results 11 to 20 of about 2,811 (218)
Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response [PDF]
The Scientific World Journal, 2014 Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining.
Chongjing Sun +3 more
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Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
Applied Sciences, 2021 The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high ...
Yalong Zhang +4 more
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Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining
IEEE AccessFrequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets ...
Xin Dai +3 more
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TKFIM: Top-K frequent itemset mining technique based on equivalence classes [PDF]
PeerJ Computer Science, 2021 Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially.
Saood Iqbal +5 more
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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 +4 more sources
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
RUGGIERI, SALVATORE, Salvatore Ruggieri
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An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm
Complexity, 2022 Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent ...
Hussein A. Alsaeedi, Ahmed S. Alhegami
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Quick mining in dense data: applying probabilistic support prediction in depth-first order [PDF]
PeerJ Computer ScienceFrequent itemset mining (FIM) is a major component in association rule mining, significantly influencing its performance. FIM is a computationally intensive nondeterministic polynomial time (NP)-hard problem.
Muhammad Sadeequllah +3 more
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DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach [PDF]
Algorithms for Molecular Biology, 2011 Background The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks.
Vingron Martin, Serin Akdes
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A pattern-growth approach for mining maximal fault-tolerant frequent itemsets [PDF]
Scientific ReportsMining fault-tolerant (FT) frequent itemsets in noisy datasets is more challenging than conventional frequent itemset mining due to the high cost of evaluating fault-tolerance conditions.
Shariq Bashir
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