Abstract
Given a dataset of clickstream extracted from e-commerce logs, can we find a clear usage of the website? Are there hidden relationships between the purchased products? Are there any discriminatory behaviors leading to the purchase? To answer these questions, we propose in this paper a new Sequential Event Pattern Mining algorithm (SEPM). The endeavor is to mine clickstream data in order to extract and analyze useful sequential patterns of clicks. Also, in order to make these patterns clearer, the time spent on each page is taken into account. SEPM maintains the items durations during the mining process and extracts patterns with the average durations of these items without multiple scans of the dataset. Our experimental results on both real and synthetic datasets indicate that SEPM is efficient and scalable.
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Kanaan, M., Kheddouci, H. (2019). Mining Patterns with Durations from E-Commerce Dataset. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_49
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DOI: https://doi.org/10.1007/978-3-030-05411-3_49
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