Abstract
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.
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Fornells, A., Armengol, E., Golobardes, E. (2008). Retrieval Based on Self-explicative Memories. In: Althoff, KD., Bergmann, R., Minor, M., Hanft, A. (eds) Advances in Case-Based Reasoning. ECCBR 2008. Lecture Notes in Computer Science(), vol 5239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85502-6_14
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DOI: https://doi.org/10.1007/978-3-540-85502-6_14
Publisher Name: Springer, Berlin, Heidelberg
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