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Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. The paper presents a novel approach to case-based classification. The algorithm is based on a notion of similarity assessment and was developed for supporting flexible retrieval of relevant information. Validity of the proposed approach is tested on real world domains, and the system's performance is compared to that of other machine learning algorithms.