The SGD approach identifies partitions of the data space associated with outstanding distributions of a given target of interest, here the acetylene conversion. SGD starts by creating statements about the candidate descriptive parameters that are only satisfied by a portion of the dataset. Such statements are, for instance, inequalities constraining the values of the descriptive parameters to some minimum or maximum thresholds to be determined during the analysis. Then, SGD identifies conjunctions of statements that result in subselections of data (SGs) that maximize an objective function. This objective function specifies how outstanding the SGs are with respect to the entire dataset based on their sizes and on the distributions of the target values in the SG and in the entire dataset. Here, we use the objective function