ELKI: A Software System for Evaluation of Subspace Clustering Algorithms

In order to establish consolidated standards in novel data mining areas, newly proposed algorithms need to be evaluated thoroughly. Many publications compare a new proposition --- if at all --- with one or two competitors or even with a so called "naïve" ad hocsolution. For the prolific field of subspace clustering, we propose a software framework implementing many prominent algorithms and, thus, allowing for a fair and thorough evaluation. Furthermore, we describe how new algorithms for new applications can be incorporated in the framework easily.