ELKI: A software system for evaluation of subspace clustering algorithms

Abstract

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 hoc solution. 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. © 2008 Springer-Verlag.