Artificial Immune System-Based Customer Data Clustering in an e-Shopping Application

  • D. N. Sotiropoulos
  • G. A. Tsihrintzis
  • A. Savvopoulos
  • M. Virvou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


We address the problem of adaptivity of the interaction between an e-shopping application and its users. Our approach is novel, as it is based on the construction of an Artificial Immune Network (AIN) in which a mutation process is incorporated and applied to the customer profile feature vectors. We find that the AIN-based algorithm yields clustering results of users’ interests that are qualitatively and quantitatively better than the results achieved by using other more conventional clustering algorithms. This is demonstrated on user data that we collected using Vision.Com, an electronic video store application that we have developed as a test-bed for research purposes.


Feature Vector Spectral Cluster Immune Network Probabilistic Latent Semantic Analysis Shopping Cart 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • D. N. Sotiropoulos
    • 1
  • G. A. Tsihrintzis
    • 1
  • A. Savvopoulos
    • 1
  • M. Virvou
    • 1
  1. 1.Department of Computer ScienceUniversity of PiraeusPiraeusGreece

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