Results 1 to 10 of about 8,898 (200)
On differentially private frequent itemset mining [PDF]
Proceedings of the VLDB Endowment, 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 ...
Chen, Zeng +2 more
openaire +3 more sources
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
doaj +2 more sources
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
doaj +1 more source
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
doaj +1 more source
Parallel Mining Algorithm of Frequent Itemset Based on N-list and DiffNodeset Structure [PDF]
Jisuanji kexue, 2023 Frequent itemset mining is a basic problem of data mining and plays an important role in many data mining applications.In order to solve the problems of the parallel frequent itemset mining algorithm(MrPrePost) in big data environment,such as algorithm ...
ZHANG Yang, WANG Rui, WU Guanfeng, LIU Hongyi
doaj +1 more source
Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation [PDF]
EAI Endorsed Transactions on Energy Web, 2021 High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database.
Pazhaniraja N +3 more
doaj +1 more source
A review on big data based parallel and distributed approaches of pattern mining
Journal of King Saud University: Computer and Information Sciences, 2022 Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility
Sunil Kumar, Krishna Kumar Mohbey
doaj +1 more source
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
doaj +1 more source
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
Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
IEEE Access, 2023 Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database.
Hong N. Dao +5 more
doaj +1 more source