A Weighted Feature C-Means Clustering Algorithm for Case Indexing and Retrieval in Cased-Based Reasoning

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-Cmeans) to group all prior cases in the case base into several clusters. In WF-Cmeans, 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.