scholarly journals An Efficient Method for Generating Local Association Rules

2015 ◽  
Vol 9 (2) ◽  
pp. 1-5
Author(s):  
F. A.Mazarbhuiya ◽  
Yusuf Perwej
2020 ◽  
Vol 393 ◽  
pp. 245-258 ◽  
Author(s):  
Xiangjun Dong ◽  
Feng Hao ◽  
Long Zhao ◽  
Tiantian Xu

2010 ◽  
Vol 108-111 ◽  
pp. 436-440
Author(s):  
Yue Shun He ◽  
Ping Du

This paper presents an adaptive support for Boolean algorithm for mining association rules, the Algorithm does not require minimum support from outside, in the mining process of the algorithm will be based on user needs the minimum number of rules automatically adjust the scope of support to produce the specific number of rules, the algorithm number of rules for the user needs to generate the rules to a certain extent, reduce excavation time, avoid the artificial blindness specified minimum support. In addition, the core of the algorithm is using an efficient method of Boolean-type mining, using the logical OR, AND, and XOR operations to generate association rules, to avoided the candidate itemsets generated In the mining process, and only need to scan the database once, so the algorithm has a certain efficiency.


2011 ◽  
Vol 22 (12) ◽  
pp. 2965-2980 ◽  
Author(s):  
Yu-Xing MAO ◽  
Tong-Bing CHEN ◽  
Bai-Le SHI

2013 ◽  
Vol 380-384 ◽  
pp. 1701-1704
Author(s):  
Da Wei Jin ◽  
Jie Song ◽  
Bin Liu ◽  
Cheng Zhao

Traditional method mining for association rules between items in large and grand data sets is inefficient. In this paper we present an efficient method called BPMRA which is based on mapreduce and partition. We have compared BPMRA algorithm based multi-node and partition based single node method and performed some experiments. It turns out that BPMRA possesses high parallelism good stability and scalability, especially suitable for mining for association rules in large and grand data sets.


2017 ◽  
Vol 48 (6) ◽  
pp. 1491-1505 ◽  
Author(s):  
Loan T. T. Nguyen ◽  
Ngoc-Thanh Nguyen ◽  
Bay Vo ◽  
Hung Son Nguyen

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