DocumentCode :
2333835
Title :
G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules
Author :
Luna, José María ; Romero, José Raúl ; Ventura, Sebastián
Author_Institution :
Dept. of Comput. Sci. & Numerical Anal., Univ. of Cordoba, Córdoba, Spain
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
This paper presents the G3PARM algorithm for mining representative association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming) and an auxiliary population made up of its best individuals who will then act as parents for the next generation. Due to the nature of G3P, the G3PARM algorithm allows us to obtain valid individuals by defining them through a context-free grammar and, furthermore, this algorithm is generic with respect to data type. We compare our algorithm to two multiobjective algorithms frequently used in literature and known as NSGA2 (Non dominated Sort Genetic Algorithm) and SPEA2 (Strength Pareto Evolutionary Algorithm) and demonstrate the efficiency of our algorithm in terms of running-time, coverage and average support, providing the user with high representative rules.
Keywords :
context-free grammars; data mining; genetic algorithms; association rules mining; auxiliary population; context-free grammar; evolutionary algorithm; grammar guided genetic programming algorithm; Association rules; Databases; Encoding; Evolutionary computation; Genetic programming; Grammar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
Type :
conf
DOI :
10.1109/CEC.2010.5586504
Filename :
5586504
Link To Document :
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