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This chapter presents a second Multi-Dimensional Scaling procedure that aims at identifying diverse views even within single individuals. The technique is applied on an existing dataset (Heidecker and Hassenzahl, 2007). It is illustrated that the - traditional - averaging analysis provides insight to only 1/6th of the total number of attributes in the example dataset. The proposed approach accounts for more than double the information obtained from the average model, and provides richer and semantically more diverse views on the set of stimuli.