Association Rule Mining Using Genetic Algorithm: The role of Estimation Parameters

Author(s):  
K. Indira ◽  
S. Kanmani
2012 ◽  
Vol 629 ◽  
pp. 730-734 ◽  
Author(s):  
Cun Liang Yan ◽  
Wei Feng Shi

Job shop scheduling problem (JSP) is the most typical scheduling problem, In the process of JSP based on genetic algorithm (GA), large amounts of data will be produced. Mining them to find the useful information is necessary. In this paper dividing, hashing and array (DHA) association rule mining algorithm is used to find the frequent itemsets which contained in the process, and extract the corresponding association rules. Concept hierarchy is used to interpret the rules, and lots of useful rules appeared. It provides a new way for JSP study.


Author(s):  
Leila Hamdad ◽  
Zakaria Ournani ◽  
Karima Benatchba ◽  
Ahcène Bendjoudi

Author(s):  
Ahcène Bendjoudi ◽  
Karima Benatchba ◽  
Leila Hamdad ◽  
Zakaria Ournani

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yang Ou ◽  
Zheng Jiang Liu ◽  
Hamid Reza Karimi ◽  
Ying Tian

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.


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