College of Computer Science and Information Engineering Zhejiang GongShang University, Hangzhou, China

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Abstract—the use of fuzzy techniques has been considered to be one of the key components of data mining systems because of the affinity with human knowledge representation. A hybridization of fuzzy sets with genetic algorithms is described for Web mining in this paper. It is based on a hybrid technique that combines the strengths of rough set theory and genetic algorithm. The algorithm through the introduction of selection operators, crossover operators and mutation operators, improves the global convergence speed, and can effectively avoid prematurity. The role of fuzzy sets in handling the different types of uncertainties/impreciseness is highlighted. Experimental results indicate that this adaptive method significantly improves the performance in Web mining.

Index Terms—Web mining, fuzzy sets, genetic algorithm, data mining

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Cite: Chunlai Chai and Biwei Li, " A Novel Association Rules Method Based on Genetic Algorithm and Fuzzy Set Strategy for Web Mining," Journal of Computers vol. 5, no. 9, pp. 1448-1455, 2010.