After reviewing some existing measures for fuzzy sets, we introduce a new informative measure for discrimination between two fuzzy sets. This discriminating measure reduces to the nonprobabilistic entropy of Deluca and Termini [7] under a special condition. The divergence measure between two sets has been defined along with a large set of properties. It has also been used to define an ambiguity (fuzziness) measure. Renyi's [17] probabilistic entropy of order a has been extended to define nonprobabilistic entropy of a fuzzy set. Various properties of this definition have also been proved. Applications of these measures to clustering, image processing, vision, etc., are highlighted.