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 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.
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