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

Producció científica: Capítol de llibreContribució a una conferènciaAvaluat per experts

12 Cites (Scopus)

Resum

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.

Idioma originalAnglès
Títol de la publicacióCase-Based Reasoning Research and Development - 7th International Conference on Case-Based Reasoning, ICCBR 2007, Proceedings
EditorSpringer Verlag
Pàgines122-136
Nombre de pàgines15
ISBN (imprès)9783540741381
DOIs
Estat de la publicacióPublicada - 2007
Esdeveniment7th International Conference on Case-Based Reasoning, ICCBR 2007 - Belfast, Northern Ireland, United Kingdom
Durada: 13 d’ag. 200716 d’ag. 2007

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum4626 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

Conferència

Conferència7th International Conference on Case-Based Reasoning, ICCBR 2007
País/TerritoriUnited Kingdom
CiutatBelfast, Northern Ireland
Període13/08/0716/08/07

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