traditional library can’t provide the service of personalized recommendation
for users. This paper used Clementine to solve this problem. Firstly, model of
K-means clustering analyze the initial data to delete the redundant data. It can
avoid scanning the database repeatedly and producing a large number of false
rules. Secondly, the paper used clustering results to perform association rule
mining. It can obtain valuable information and achieve the service of
Cite this paper
J. Lina and M. Zhiyong, "The Application of Book Intelligent Recommendation Based on the Association Rule Mining of Clementine," Journal of Software Engineering and Applications, Vol. 6 No. 7, 2013, pp. 30-33. doi: 10.4236/jsea.2013.67B006.
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