期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：2
DOI：10.14569/IJACSA.2022.0130249
语种：English
出版社：Science and Information Society (SAI)
摘要：Nowadays e-commerce environment plays an important role to exchange commodity knowledge between consumers commonly with others. Accurately predicting customer purchase patterns in the e-commerce market is one of the critical applications of data mining. In order to achieve high profit in e-commerce, the relationship between customer and merchandise are very important. Moreover, many e-commerce websites increase rapidly and instantly and competition has become just a mouse-click away. That is why the importance of staying in the business, and improving the profit needs to accurately predict purchase behavior and target their customers with personalized services according to their preferences. In this paper, a data mining model has been proposed to enhance the accuracy of predicting and to find association rules for frequent item sets. Also, K-means clustering algorithm has been used to reduce the size of the dataset in order to enhance the runtime for the proposed model. The proposed model has used four different classifiers which are C4.5, J48, CS-MC4, and MLR. Also, Apriori algorithm to provide recommendations for items based on previous purchases. The proposed model has been tested on Northwind trader’s dataset and the results archives accuracy equal 95.2% when the number of clusters were 8.
