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.
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Achtert, E., Kriegel, HP., Zimek, A. (2008). ELKI: A Software System for Evaluation of Subspace Clustering Algorithms. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_41
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DOI: https://doi.org/10.1007/978-3-540-69497-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69476-2
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