Retrieval based on self-explicative memories

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2 Cites (Scopus)


One of the key issues in Case-Based Reasoning (CBR) systems is the efficient retrieval of cases when the case base is huge and/or it contains uncertainty and partial knowledge. We tackle these issues by organizing the case memory using an unsupervised clustering technique to identify data patterns for promoting all CBR steps. Moreover, another useful property of these patterns is that they provide to the user additional information about why the cases have been selected and retrieved through symbolic descriptions. This work analyses the introduction of this knowledge in the retrieve phase. The new strategies improve the case retrieval configuration procedure.

Idioma originalAnglès
Títol de la publicacióAdvances in Case-Based Reasoning - 9th European Conference, ECCBR 2008, Proceedings
Nombre de pàgines15
Estat de la publicacióPublicada - 2008
Esdeveniment9th European Conference on Case-Based Reasoning, ECCBR 2008 - Trier, Germany
Durada: 1 de set. 20084 de set. 2008

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum5239 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349


Conferència9th European Conference on Case-Based Reasoning, ECCBR 2008


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