MDCGen v2

Generator of synthetic n-dimensional datasets for clustering and outlier detection

MdcGen allows a high-flexibility for parameterization, implementing clusters with varied shapes and generated by diverse underlying distributions. The tool enables the creation of clusters based on multivariate distributions but also clusters where distributions directly determine cluster intra-distances (i.e., the distance of objects to cluster centroids). Additionally, MDCGen implements classic functionalities, e.g., customization of cluster-separation, overlap control, addition of outliers and noisy features, correlated variables, rotations, and dataset quality evaluations, among others.

In order to allow a broad generation variety and flexibility, some configurations might create meaningless or useless datasets. Therefore, some experience dealing with the parameters is advisable (parameters are widely explained in the documentation). To validate the dataset, Silhouette evaluations provide performance indices to assess if the generated data follows a clear cluster-like structure.

Denis Ojdanic revised and improved MDCGen v1, developing the current MDCGen v2.