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.
['In-Chul Jung', 'M. Alex Syaekhoni', 'Young S. Kwon']
A Practical Approach to the Shopping Path Clustering