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Most of the recently discussed and commercially introduced test stimulus data compression techniques are based on low care bit densities found in typical scan test vectors. Data volume and test times are reduced primarily by compressing the don't-care bit information. The original care bit density, hence, dominates the theoretical compression limits. Further compression can be achieved by focusing on opportunities to compress care bit information in addition to the don't-care bit information. We discuss at a conceptual level how data compression based on test cube clustering effects, as used in weighted random pattern methods, could be combined with care bit oriented methods to achieve multilevel test stimulus compression.