A Methodology for Analyzing Case Retrieval from a Clustered Case Memory

  • Albert Fornells
  • Elisabet Golobardes
  • Josep Maria Martorell
  • Josep Maria Garrell
  • Núria Macià
  • Ester Bernadó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4626)


Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.


Case Retrieval Case Memory Organization Soft Case- Based Reasoning Complexity Measures Self-Organization Maps 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Albert Fornells
    • 1
  • Elisabet Golobardes
    • 1
  • Josep Maria Martorell
    • 1
  • Josep Maria Garrell
    • 1
  • Núria Macià
    • 1
  • Ester Bernadó
    • 1
  1. 1.Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 BarcelonaSpain

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