Abstract
The efficiency of many data driven e-commerce system may be compromised by an abundance of data. In this chapter we discuss how knowledge discovery and data mining techniques can be useful in improving the scalability of data driven e-commerce systems. In particular we focus on improving scalability via dimensionality reduction and improving the information view experienced by each user. To address these issues, we cover several common data mining problems, including feature selection, clustering, classification, and association rule discovery and present several scalable methods and algorithms that address each of those problems. Numerous examples are included to illustrate the key ideas.
This work was supported by the National Science Foundation under grant DMI-0075575.
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Olafsson, S. (2003). Improving Scalability of E-Commerce Systems with Knowledge Discovery. In: Prabhu, V., Kumara, S., Kamath, M. (eds) Scalable Enterprise Systems. Integrated Series in Information Systems, vol 3. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0389-7_6
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DOI: https://doi.org/10.1007/978-1-4615-0389-7_6
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