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ABSTRACT
This article describes a novel way of combining data mining techniques on Internet data in order to discover actionable marketing intelligence in electronic commerce scenarios. The data that is considered not only covers various types of server and web meta information, but also marketing data and knowledge. Furthermore, heterogeneity resolution thereof and Internet- and electronic commerce-specific pre-processing activities are embedded. A generic web log data hypercube is formally defined and schematic designs for analytical and predictive activities are given. From these materialised views, various online analytical web usage data mining techniques are shown, which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes.
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Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Guandong Xu , Yanchun Zhang , Jiangang Ma , Xiaofang Zhou, Discovering user access pattern based on probabilistic latent factor model, Proceedings of the sixteenth Australasian database conference, p.27-35, January 01, 2005, Newcastle, Australia
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