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Data Mining for Diverse E-Commerce Applications

  • Amar Gupta
  • Sanjeev Vadhavkar
  • Jason Yeung
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)

Abstract

In their effort to incorporate electronic commerce capabilities, many organizations have established comprehensive data warehouses to integrate operational data with customers, suppliers, and other channel partners. Emerging data mining techniques enable organizations to prioritize and structure information from these warehouses. Through its discovery of changes, associations, rules, anomalies, and patterns, data mining can lead to significant business benefits when combined with current information systems. This chapter illustrates the pivotal role that data mining plays in an electronic commerce environment by highlighting two case studies in which neural network-based data mining techniques were used for inventory optimization. Issues such as automated identification of input-output lags, data augmentation, and optimal neural network architectures are discussed.

Keywords

Data Mining Blast Furnace Data Mining Technique Electronic Commerce Normalize Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Amar Gupta
    • 1
  • Sanjeev Vadhavkar
    • 1
  • Jason Yeung
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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