Case-based reasoning
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An effective way to represent quadtrees
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A feature selection technique for classificatory analysis
Pattern Recognition Letters
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
A case-based reasoning system for PCB defect prediction
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
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A successful Case-Based Reasoning (CBR) system highly depends on how to design an accurate and efficient case retrieval mechanism. In this research we propose a Weighted Feature C-means clustering algorithm (WF-C-means). to group all prior cases in the case base into several clusters. In WF-C-means, the weight of each feature is automatically adjusted based on the importance of the feature to clustering quality. After executing WF-C-means, the dissimilarity definition adopted by K-Nearest Neighbor (KNN) search method to retrieve similar prior cases for a new case becomes refined and objective because the weights of all features adjusted by WF-C-means can be involved in the dissimilarity definition. On the other hand, based on the clustering result of WF-C-means, this research proposes a cluster-based case indexing scheme and its corresponding case retrieval strategy to help KNN retrieving the similar prior cases efficiently. Through our experiments, the efforts of this research are useful for real world CBR systems.