QTclustering {FCPS}

Stochastic QT Clustering

Description

Stochastic quality clustering of [Heyer et al., 1999] with an improved implementation by [Scharl/Leisch, 2006].

Usage

QTclustering(Data,Radius,PlotIt=FALSE,...)

Arguments

Data

[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.

Radius

Maximum radius of clusters. If NULL, automatic estimation can be done with [Thrun et al., 2016] if not otherwise set.

PlotIt

Default: FALSE, if TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls

...

Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Value

List of

Cls

[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering. Points which cannot be assigned to a cluster will be reported with 0.

Object

Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Heyer et al., 1999] Heyer, L. J., Kruglyak, S., & Yooseph, S.: Exploring expression data: identification and analysis of coexpressed genes, Genome research, Vol. 9(11), pp. 1106-1115. 1999.

[Scharl/Leisch, 2006] Scharl, T., & Leisch, F.: The stochastic QT-clust algorithm: evaluation of stability and variance on time-course microarray data, in Rizzi , A. & Vichi, M. (eds.), Proc. Proceedings in Computational Statistics (Compstat), pp. 1015-1022, Physica Verlag, Heidelberg, Germany, 2006.

[Thrun et al., 2016] Thrun, M. C., Lerch, F., Loetsch, J., & Ultsch, A. : Visualization and 3D Printing of Multivariate Data of Biomarkers, in Skala, V. (Ed.), International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Vol. 24, Plzen, 2016.

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, In Baier, D. & Werrnecke, K. D. (Eds.), Innovations in classification, data science, and information systems, (Vol. 27, pp. 91-100), Berlin, Germany, Springer, 2005.

Examples

data('Hepta')
out=QTclustering(Hepta$Data,Radius=NULL,PlotIt=FALSE)