Use of genetic algorithm to extract associative rules

Online shopping is a form of electronic commerce which allows consumers to directly buy goods or services from a seller over the Internet using a web browser. Consumers find a product of interest by visiting the website of the retailer directly or by searching among alternative vendors using a shopping search engine, which displays the same product's availability and pricing at different e-retailers. This kind of marketing generates a numeroustransaction and data which are potentially valuable Using data mining is an alternative to discover frequent patterns and association rules from datasets.. In this paper, we use data mining techniques for discovering frequent customers‟ buying patterns from a CustomerRelationship Management database. There are lots of algorithms for this purpose, such as Apriori and FP- Growth. However, they may not have efficient performance when the data is big, therefore various meta-heuristic methods can be an alternative. In this paper we first excerpt loyal customers by using RFM criterion to face more reliable answers and create relevant dataset. Then association rules are discovered using proposed genetic algorithm. The results showed that our proposed approach is more efficient and have some distinction in compare with other methods mentioned in this research.

Keywords:

Feature selection , Genetic algorithm , Data mining , Association rules , customer relationship management