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Case-Based Reasoning Research and Development

Volume 4626 of the series Lecture Notes in Computer Science pp 122-136

A Methodology for Analyzing Case Retrieval from a Clustered Case Memory

  • Albert FornellsAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona
  • , Elisabet GolobardesAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona
  • , Josep Maria MartorellAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona
  • , Josep Maria GarrellAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona
  • , Núria MaciàAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona
  • , Ester BernadóAffiliated withGrup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 Barcelona

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Abstract

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.

Keywords

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