One of the problems solved by machine learning based techniques is symbolic data analysis and concept formation. We report on the program FAVORIT, which achieves a performance improvement over its predecessors (such as UNIMEM) by means of a simple mechanism mimicking the shortcomings of human learning: aging of knowledge and forgetting. When applied to large and noisy data sets, these characteristics enable efficient restructuring and pruning of the internal knowledge structures. The paper contains a brief description of the program, together with the rationale behind its philosophy, as well as a simple case study.