scatteR: Generating instance space based on scagnostics

Modern synthetic data generators consist of model-based methods where the focus is primarily on tuning the parameters of the model and not on specifying the structure of the data itself. Scagnostics is an exploratory graphical method, capable of encapsulating the structure of bivariate data through graph-theoretic measures. An inverse scagnostic measure would therefore provide an entry point to generate datasets based on the characteristics of instance space rather than a model-based simulation approach. scatteR is a novel data generation method with controllable characteristics based on scagnostic measurements. We have used a Generalized Simulated Annealing optimizer iteratively to discover the optimal arrangement of data points in each iteration that minimizes the distance between the current and target measurements. Generally, as a pedagogical tool, scatteR can be used to generate datasets to teach statistical methods. Based on the results of this study, scatteR is capable of generating 50 data points in under 30 seconds with a 0.05 Root Mean Squared Error on average.