Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data

Abstract

Artificially generated data sets are present in many data mining and machine learning publications in the experimental section. One of the reasons to use synthetic data is, that scientists can express their understanding of a “ground truth”, having labels and thus an expectation of what an algorithm should be able to detect. This permits also a degree of control to create data sets which either emphasize the strengths of a method or reveal its weaknesses and thus potential targets for improvement. In order to develop methods which detect linear correlated clusters, the necessity of generating such artificial clusters is indispensable. This is mostly done by command-line based scripts which may be tedious since they demand from users to ‘visualize’ in their minds how the correlated clusters have to look like and be positioned within the data space. We present in this work RAIL, a generator for Reproducible Artificial Interactive Linear correlated data. With RAIL, users can add multiple planes into a data space and arbitrarily change orientation and position of those planes in an interactive fashion. This is achieved by manipulating the parameters describing each of the planes, giving users immediate feedback in real-time. With this approach scientists no longer need to imagine their data but can interactively explore and design their own artificial data sets containing linear correlated clusters. Another convenient feature in this context is that the data is only generated when the users decide that their design phase is completed. If researchers want to share data, a small file is exchanged containing the parameters which describe the clusters through information such as e.g. their Hessian-Normal-Form or number of points per cluster, instead of sharing several large csv files.