MoGclustering: Mixture of Gaussians Clustering using EM

In FCPS: Fundamental Clustering Problems Suite

Mixture of Gaussians Clustering using EM

Description

MixtureOfGaussians (MoG) clustering based on Expectation Maximization (EM) of [Chen et al., 2012] or algorithms closely resembling EM of [Benaglia/Chauveau/Hunter, 2009].

Usage

MoGclustering(Data,ClusterNo=2,Type,PlotIt=FALSE,Silent=TRUE,...)

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.
ClusterNo	A number k which defines k different clusters to be built by the algorithm.
Type	string defining approach to select: initialization approach of "EM" or "kmeans" of [Chen et al., 2012], or other methods "mvnormalmixEM" [McLachlan/Peel, 2000], "npEM"[Benaglia et al., 2009] or its extension "mvnpEM" [Chauveau/Hoang, 2016].
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
Silent	(optional) Boolean: print output or not (Default = FALSE = no output)
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used, see package mixtools EMCluster or mixtools for details.

Details

Algorithms for clustering through EM or its close resembles.

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

MoG used in [Thrun, 2017] was renamed to ModelBasedClustering in this package. Type="mvnormalmixEM" sometimes fails

Author(s)

Michael Thrun

References

[Chen et al., 2012] Chen, W., Maitra, R., & Melnykov, V.: EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution, R Package, URL http://cran. r-project. org/package= EMCluster, 2012.

[Chauveau/Hoang, 2016] Chauveau, D., & Hoang, V. T. L.: Nonparametric mixture models with conditionally independent multivariate component densities, Computational Statistics & Data Analysis, Vol. 103, pp. 1-16. 2016.

[Benaglia et al., 2009] Benaglia, T., Chauveau, D., and Hunter, D. R.: An EM-like algorithm for semi-and nonparametric estimation in multivariate mixtures. Journal of Computational and Graphical Statistics, 18(2), pp. 505-526, 2009.

[McLachlan/Peel, 2000] D. McLachlan, G. J. and Peel, D.: Finite Mixture Models, John Wiley and Sons, Inc, 2000.

See Also

ModelBasedClustering

Examples

data('Hepta')
Data = Hepta$Data
out=MoGclustering(Data,ClusterNo=7,Type="EM",PlotIt=FALSE)
V=out$Cls


V1 = MoGclustering(Data,ClusterNo=7,Type="mvnpEM")
Cls1 = V1$Cls

V2 = MoGclustering(Data,ClusterNo=7,Type="npEM")
Cls2 = V2$Cls


## Not run: 
#does not work always
  V3 = MoGclustering(Data,ClusterNo=7,Type="mvnormalmixEM")
  Cls3 = V3$Cls

## End(Not run)

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