Abstract
Electronic commerce provides all the right ingredients for successful data mining (the Good). Web logs, however, are at a very low granularity level, and attempts to mine e-commerce data using only web logs often result in little interesting insight (the Bad). Getting the data into minable formats requires significant pre-processing and data transformations (the Ugly). In the ideal e-commerce architecture, high level events are logged, transformations are automated, and data mining results can easily be understood by business people who can take action quickly and efficiently. Lessons, stories, and challenges based on mining real data at Blue Martini Software will be presented.
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© 2001 Springer-Verlag Berlin Heidelberg
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Kohavi, R. (2001). Mining E-Commerce Data: The Good, the Bad, and the Ugly. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_2
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DOI: https://doi.org/10.1007/3-540-45357-1_2
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