An FCA approach to mining quantitative association rules from multi-relational data

2017 ◽  
Vol 6 (4) ◽  
pp. 366
Author(s):  
Masahiro Nagao ◽  
Hirohisa Seki
Keyword(s):  
Association Rules ◽  
Relational Data ◽  
Quantitative Association Rules

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

2018 ◽  
Vol 153 ◽  
pp. 176-192 ◽  
Author(s):  
D. Martín ◽  
M. Martínez-Ballesteros ◽  
D. García-Gil ◽  
J. Alcalá-Fdez ◽  
F. Herrera ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Mapreduce Framework ◽  
Quantitative Association Rules

A novel hybrid GA–PSO framework for mining quantitative association rules

2019 ◽  
Vol 24 (6) ◽  
pp. 4645-4666 ◽  
Author(s):  
Fateme Moslehi ◽  
Abdorrahman Haeri ◽  
Francisco Martínez-Álvarez
Keyword(s):  
Association Rules ◽  
Quantitative Association Rules
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