Bottom-Up Induction of Feature Terms
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
The Knowledge Engineering Review
A Methodology for Analyzing Case Retrieval from a Clustered Case Memory
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Explanation of a Clustered Case Memory Organization
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Case retrieval through multiple indexing and heuristic search
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Expert Systems with Applications: An International Journal
Unsupervised case memory organization: analysing computational time and soft computing capabilities
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Integration of a Methodology for Cluster-Based Retrieval in jColibri
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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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.