MSTclustering: MST-kNN clustering algorithm [Inostroza-Ponta, 2008].

In FCPS: Fundamental Clustering Problems Suite

MST-kNN clustering algorithm [Inostroza-Ponta, 2008].

Description

Performs the MST-kNN clustering algorithm which generate a clustering solution with automatic k determination using two proximity graphs: Minimal Spanning Tree (MST) and k-Nearest Neighbor (kNN) which are recursively intersected.

Usage

MSTclustering(DataOrDistances, DistanceMethod = "euclidean",PlotIt=FALSE, ...)

Arguments

DataOrDistances	Either [1:n,1:n] symmetric distance matrix or [1:n,1:d] not symmetric data matrix of n cases and d variables
DistanceMethod	Optional distance method of data, default is euclid, see parDist for details
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
...	Optional, further arguments for mst.knn

Details

Does not work on Hepta with euclidean distances.

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

Author(s)

Michael Thrun

References

[Inostroza-Ponta, 2008] Inostroza-Ponta, M.: An integrated and scalable approach based on combinatorial optimization techniques for the analysis of microarray data, University of Newcastle, ISBN, 2008

See Also

mst.knn

Examples

data(Hepta)

MSTclustering(Hepta$Data)

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