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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Research on association analysis between electricity consumption behaviors and weather factors based on mapreduce [PDF]
Scientific ReportsThe change of weather factors will lead to great changes in users’ electricity consumption behaviors. In order to discover the associations between users’ electricity consumption behavior and weather factors, and meet the needs of efficient mining of ...
Yuehua Yang, Yun Wu
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IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments [PDF]
HeliyonPeriodic high-utility itemset (PHUI) mining can extend beyond the conventional approach of high-utility itemset mining by uncovering recurring customer purchase behaviors common in real-life scenarios (e.g., buying apples and oranges every three days or ...
Huiwu Huang, Shixi Chen, Jiahui Chen
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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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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
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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
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A Bitmap Approach for Mining Erasable Itemsets
IEEE Access, 2021 Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of ...
Tzung-Pei Hong +4 more
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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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