MDCGen: Multidimensional Dataset Generator for Clustering

Félix Iglesias (), Tanja Zseby (), Daniel Ferreira () and Arthur Zimek ()
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Félix Iglesias: TU Wien
Tanja Zseby: TU Wien
Daniel Ferreira: TU Wien
Arthur Zimek: Department of Mathematics and Computer Science (IMADA)

Journal of Classification, 2019, vol. 36, issue 3, No 13, 599-618

Abstract: Abstract We present a tool for generating multidimensional synthetic datasets for testing, evaluating, and benchmarking unsupervised classification algorithms. Our proposal fills a gap observed in previous approaches with regard to underlying distributions for the creation of multidimensional clusters. As a novelty, normal and non-normal distributions can be combined for either independently defining values feature by feature (i.e., multivariate distributions) or establishing overall intra-cluster distances. Being highly flexible, parameterizable, and randomizable, MDCGen also implements classic pursued features: (a) customization of cluster-separation, (b) overlap control, (c) addition of outliers and noise, (d) definition of correlated variables and rotations, (e) flexibility for allowing or avoiding isolation constraints per dimension, (f) creation of subspace clusters and subspace outliers, (g) importing arbitrary distributions for the value generation, and (h) dataset quality evaluations, among others. As a result, the proposed tool offers an improved range of potential datasets to perform a more comprehensive testing of clustering algorithms.

Keywords: Clustering; Dataset generator; Synthetic data (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00357-019-9312-3

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