TandemClustering: Tandem Clustering

In FCPS: Fundamental Clustering Problems Suite

Tandem Clustering

Description

Summarizes clustering methods that combine k-means clustering and principal component analysis (PCA).

Usage

TandemClustering(Data,ClusterNo,Type="Reduced",PlotIt=FALSE,...)

Arguments

Data	[1:n,1:d] matrix containing the 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	Reduced: Reduced k-means (RKM) [De Soete/Carroll, 1994]. Factorial: Factorial k-means (FKM) [Vichi/Kiers, 2001]. KernelPCA: Kernel PCA with minimum normalized-cut hyperplanes [Hofmeyr/Pavlidis, 2019].
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.

Details

If ClusterNo exceeds the number of dimensions, the function is called recursively with ClusterNo = 2. In each iteration, the largest cluster is split again until the requested number of clusters is reached.

"KernelPCA" additionally uses the kernlab package and follows the fifth example in the extension section on page 18 of [Hofmeyr/Pavlidis, 2019].

The idea of using non-PCA projections for clustering was first proposed by [Bock, 1987], but, to the author's knowledge, it was not applied to data. The first systematic comparison with the projection-pursuit methods ProjectionPursuitClustering and AutomaticProjectionBasedClustering can be found in [Thrun/Ultsch, 2018].

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

[De Soete/Carroll, 1994] De Soete, G., & Carroll, J. D.: K-means clustering in a low-dimensional Euclidean space, New approaches in classification and data analysis, (pp. 212-219), Springer, 1994.

[Hofmeyr/Pavlidis, 2019] Hofmeyr, D., & Pavlidis, N.: PPCI: an R Package for Cluster Identification using Projection Pursuit, The R Journal, 2019.

[Vichi/Kiers, 2001] Vichi, M., & Kiers, H. A.: Factorial k-means analysis for two-way data, Computational Statistics & Data Analysis, Vol. 37(1), pp. 49-64. 2001.

[Thrun/Ultsch, 2018] Thrun, M. C., & Ultsch, A.: Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data, Journal of Classification, Vol. in revision, 2018.

[Bock, 1987] Bock, H.: On the interface between cluster analysis, principal component analysis, and multidimensional scaling, Multivariate statistical modeling and data analysis, (pp. 17-34), Springer, 1987.

Examples

data("Hepta")
out <- TandemClustering(
  Hepta$Data,
  ClusterNo = 7,
  PlotIt = FALSE
)

FCPS documentation built on Oct. 3, 2026, 9:06 a.m.