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Finding Internet browsing patterns is a current hot topic, with expected benefits in many areas, marketing and business intelligence among others. Discovering user's internet habits might improve fields like chained-publicity, e-commerce and media optimization. The large amount of data contained in log files that is currently being analyzed to find user's patterns require efficient and scalable data mining solutions. This paper proposes an algorithm to identify the most frequent route followed by Internet users, based on a specific combination of simple statistical and vectorial operators that provides exact solution with a really cheap computational cost. In the paper, the performance is compared with other two algorithms and an application to a real case study in the field of bussiness intelligence and chained publicity is presented.
CitationPalomino, A.; Gibert, Karina. Web pattern detection for business intelligence with data mining. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development. Recent Advances and Applications". Barcelona: IOS Press, 2014, p. 277-280.
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