Instance-Based Learning Algorithms
Machine Learning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining soft-matching association rules
Proceedings of the eleventh international conference on Information and knowledge management
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Classification Using Similarity-Based Retrieval
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Survey of Improving K-Nearest-Neighbor for Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
IEEE Transactions on Knowledge and Data Engineering
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Retrieval is often considered the most important task in Case-Based Reasoning (CBR), since it lays the foundation for overall performance of CBR systems. In CBR, a typical retrieval strategy is realized through similarity knowledge encoded in similarity measures. This strategy is often called similarity-based retrieval (SBR). This paper proposes and validates that association analysis techniques can be used to improve SBR. We propose a retrieval strategy USIMSCAR that performs the retrieval task by integrating similarity and association knowledge.We show its reliability, in comparison with several retrieval methods implementing SBR, using datasets from UCI ML Repository