clusterlab: Flexible Gaussian Cluster Simulator

Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers. The algorithm is highly customizable and well suited to testing class discovery tools across a range of fields.

Version: 0.0.2.5
Depends: R (≥ 3.4.0)
Imports: ggplot2, reshape
Suggests: knitr
Published: 2018-10-05
Author: Christopher R John
Maintainer: Christopher R John <chris.r.john86 at gmail.com>
License: AGPL-3
NeedsCompilation: no
CRAN checks: clusterlab results

Downloads:

Reference manual: clusterlab.pdf
Vignettes: clusterlab
Package source: clusterlab_0.0.2.5.tar.gz
Windows binaries: r-devel: clusterlab_0.0.2.5.zip, r-release: clusterlab_0.0.2.5.zip, r-oldrel: clusterlab_0.0.2.5.zip
OS X binaries: r-release: clusterlab_0.0.2.5.tgz, r-oldrel: clusterlab_0.0.2.5.tgz
Old sources: clusterlab archive

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