Abstract
This paper proposes a new clustering approach for customer shopping paths. The approach is based on the Apriori algorithm and LCS (Longest Common Subsequence) algorithms. We devised new similarity and performance measurements for the clustering. In this approach, we do not require data normalization for preprocessing, which leads to an easy and practical application and implementation of the proposed approach. The experiment results show that the proposed approach performs well compared with k-medoids clustering.
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© 2015 Springer International Publishing Switzerland
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Jung, IC., Alex Syaekhoni, M., Kwon, Y.S. (2015). A Practical Approach to the Shopping Path Clustering. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_65
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DOI: https://doi.org/10.1007/978-3-319-19066-2_65
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