Computational geometry: an introduction
Computational geometry: an introduction
A statistical similarity measure
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical representations of collections of small rectangles
ACM Computing Surveys (CSUR)
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
A more cost effective algorithm for finding perfect hash functions
CSC '89 Proceedings of the 17th conference on ACM Annual Computer Science Conference
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Information Retrieval
Experience, Memory and Reasoning
Experience, Memory and Reasoning
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Case-Based Reasoning for Antibiotics Therapy Advice
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Evaluation of a Case-Based Antibiotics Therapy Adviser
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Efficiently Implementing Episodic Memory
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Adaptation and medical case-based reasoning focusing on endocrine therapy support
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Optimising retrieval phase in CBR through Pearl and JLO algorithms for medical diagnosis
International Journal of Advanced Intelligence Paradigms
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One of the major issues confronting case-based reasoning (CBR) is rapid retrieval of similar cases from a large case base. This paper describes three algorithms which address this problem. The first algorithm works with quantitative cases using a graphical paradigm where the hyperspace containing the cases is divided into smaller and smaller hypercubes. The retrieval time for this algorithm is O(Log(N)), where N is the number of cases. The second algorithm works on qualitative data by efficiently retrieving cases based on every necessary combination of case attributes. Its retrieval time varies only with respect to the number of attributes. The third algorithm is a combination of the previous two and allows retrieval of cases consisting of both quantitative and qualitative information. The algorithms described in this paper are the first practical algorithms designed for case based retrieval on very large numbers of cases. The algorithms easily handle case bases containing millions of cases or more.