Tutmez, B.Tercan, A. E.Kaymak, U.2024-08-042024-08-0420080038-223Xhttps://hdl.handle.net/11616/94500We present a novel, hybrid algorithm for quantifying the ore grade variability that has central importance in ore reserve estimation. The proposed algorithm has three stages: (1) fuzzy clustering, (2) similarity measure, and (3) grade estimation. The method first considers data clustering, and then uses the clustering information for quantifying the ore grades by means of a cumulative point semimadogram function. The method provides a measure of similarity and gives an indication of the regional heterogeneity. In addition, grade estimations can be obtained at different levels of similarity using a weighting function, which is the standard regional dependence function (SRDF).eninfo:eu-repo/semantics/closedAccessgradefuzzy clusteringsimilarity measurepoint madogramweighting functionArticle108283902-s2.0-41849111997N/AWOS:000254680300005Q4