PenalizedRegressionBasedClustering: Penalized Regression-Based Clustering of [Wu et al., 2016].

In FCPS: Fundamental Clustering Problems Suite

Penalized Regression-Based Clustering of [Wu et al., 2016].

Description

Clustering is performed through penalized regression with grouping pursuit

Usage

PenalizedRegressionBasedClustering(Data, FirstLambda, 

SecondLambda, Tau, 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.
FirstLambda	Set 1 for quadratic penalty based algorithm, 0.4 for revised ADMM.
SecondLambda	The magnitude of grouping penalty.
Tau	Tuning parameter: tau, related to grouping penalty.
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 for PRclust, enables also usage of [Pan et al., 2013].

Details

Parameters are rather challenging to choose.

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

Note

Data matrix is internally transposed in order to fit the definition of the algorithm.

Author(s)

Michael Thrun

References

[Pan et al., 2013] Pan, W., Shen, X., & Liu, B.: Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty, The Journal of Machine Learning Research, Vol. 14(1), pp. 1865-1889. 2013.

[Wu et al., 2016] Wu, C., Kwon, S., Shen, X., & Pan, W.: A new algorithm and theory for penalized regression-based clustering, The Journal of Machine Learning Research, Vol. 17(1), pp. 6479-6503. 2016.

Examples

data(Hepta)
Data=Hepta$Data
out=PenalizedRegressionBasedClustering(Data,0.4,1,2,PlotIt=FALSE)
table(out$Cls,Hepta$Cls)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.