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.

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Description file content

Package
clusterlab
Title
Flexible Gaussian Cluster Simulator
Version
0.0.2.4
Date
2018-08-28
Author
Christopher R John
Maintainer
Christopher R John
Depends
R (>= 3.4.0)
Description
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.
License
AGPL-3
Encoding
UTF-8
LazyData
true
Imports
ggplot2, reshape
Suggests
knitr
VignetteBuilder
knitr
RoxygenNote
6.0.1
NeedsCompilation
no
Packaged
2018-09-12 06:59:13 UTC; christopher
Repository
CRAN
Date/Publication
2018-09-12 07:10:02 UTC

install.packages('clusterlab')

0.0.2.4

10 days ago

Christopher R John

AGPL-3

Depends on

R (>= 3.4.0)

Imports

ggplot2, reshape

Suggests

knitr

Discussions