Abstract
The everlasting growth of the web in terms of, e.g., amount of information, size of the net, and number of users is demanding for tools that help to tackle content (re)structuring, discover navigation patterns of users, support marketing activities of sellers (e.g., advertising and cross selling), and attract potential customers in this new environment. This paper describes how user navigation paths can be extracted from raw web logfiles and how frequent subsequences can be discovered in those paths. To better cope with navigational behaviour in the large, generalized sequences containing wildcards are used and a new algorithm for mining all frequent generalized subsequences from a given database is presented.
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Gaul, W., Schmidt-Thieme, L. (2000). Frequent Generalized Subsequences — A Problem From Web Mining. In: Gaul, W., Opitz, O., Schader, M. (eds) Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58250-9_35
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DOI: https://doi.org/10.1007/978-3-642-58250-9_35
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