Abstract
Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of the discovery of sequential access patterns from web logs. This paper introduces a new algorithm called M i DAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify generic navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
This research has partly been funded by the ESPRIT project No 26749 (MIMIC — Mining the Internet for Marketing IntelligenCe).
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Baumgarten, M., Büchner, A.G., Anand, S.S., Mulvenna, M.D., Hughes, J.G. (2000). User-Driven Navigation Pattern Discovery from Internet Data. In: Masand, B., Spiliopoulou, M. (eds) Web Usage Analysis and User Profiling. WebKDD 1999. Lecture Notes in Computer Science(), vol 1836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44934-5_5
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