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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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
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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DISCOVERING CONFUSING FREQUENT ITEMSETS
Tạp chí Khoa học Đại học Đà Lạt, 2018 Frequent itemset mining is one of the most important research areas in the field of association rule mining. Exploiting frequent itemsets at different abstraction levels of data will yield valuable knowledge.
Huỳnh Thành Lộc
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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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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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Frequent Itemset Mining of High-Dimensional Data Based on MapReduce [PDF]
Jisuanji gongcheng, 2022 In the mining process of large-scale high-dimensional data, the traditional data mining algorithm has some problem, such as low accuracy of data feature capture, unbalanced node load, frequent data interaction, and low compactness of frequent itemset ...
ZHAO Xincan, ZHU Yun, MAO Yimin
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A Parallel Apriori Algorithm and FP- Growth Based on SPARK [PDF]
ITM Web of Conferences, 2021 Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets.
Gupta Priyanka, Sawant Vinaya
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IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES [PDF]
Applied Computer Science, 2021 Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets.
Pannangi Naresh, R. Suguna
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