An Efficient Tree-Based Algorithm for Mining High Average-Utility Itemset
IEEE Access, 2019 High-utility itemset mining (HUIM), which is an extension of well-known frequent itemset mining (FIM), has become a key topic in recent years. HUIM aims to find a complete set of itemsets having high utilities in a given dataset.
Irfan Yildirim, Mete Celik
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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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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
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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
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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
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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
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New Spark solutions for distributed frequent itemset and association rule mining algorithms
Cluster Computing, 2023 The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information ...
Carlos Fernandez-Basso +2 more
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Data Poisoning Attacks to Locally Differentially Private Frequent Itemset Mining Protocols [PDF]
Conference on Computer and Communications SecurityLocal differential privacy (LDP) provides a way for an untrusted data collector to aggregate users' data without violating their privacy. Various privacy-preserving data analysis tasks have been studied under the protection of LDP, such as frequency ...
Wei Tong +3 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
Feng Tao +5 more
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