PrivBUD-Wise: Differentially Private Frequent Itemsets Mining in High-Dimensional Databases

2019 ◽  
pp. 110-124 ◽  
Author(s):  
Jingxin Xu ◽  
Kai Han ◽  
Pingping Song ◽  
Chaoting Xu ◽  
Fei Gui
Keyword(s):  
Frequent Itemsets ◽  
High Dimensional ◽  
Frequent Itemsets Mining

Frequent Itemsets Mining of SCADA Data Based on FP-Growth Algorithm

2020 ◽  
Author(s):  
Xiaolei Ma ◽  
Yongguang Li ◽  
Ran Liu ◽  
Yanjun Zhang ◽  
Liya Ma ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Frequent Itemsets Mining

scholarly journals Application of Frequent Itemsets Mining to Analyze Patterns of One-Stop Visits in Taiwan

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e14824 ◽  
Author(s):  
Chun-Yi Tu ◽  
Tzeng-Ji Chen ◽  
Li-Fang Chou
Keyword(s):  
Frequent Itemsets ◽  
One Stop ◽  
Frequent Itemsets Mining

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

Using hashing and lexicographic order for Frequent Itemsets Mining on data streams

2019 ◽  
Vol 125 ◽  
pp. 58-71 ◽  
Author(s):  
Lázaro Bustio-Martínez ◽  
Martín Letras-Luna ◽  
René Cumplido ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe ◽  
...  
Keyword(s):  
Data Streams ◽  
Lexicographic Order ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining
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