mstknnclust (version 0.3.2)

mst.knn: Performs the MST-kNN clustering algorithm

Description

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

To create MST, Prim algorithm is used. To create kNN, distance.matrix passed as input is considered.

Usage

mst.knn(distance.matrix, suggested.k)

Value

A list with the elements

cnumber

A numeric value representing the number of clusters of the solution.

cluster

A named vector of integers from 1:cnumber representing the cluster to which each object is assigned.

partition

A partition matrix order by cluster where are shown the objects and the cluster where they are assigned.

csize

A vector with the cardinality of each cluster in the solution.

network

An object of class "igraph" as a network representing the clustering solution.

Arguments

distance.matrix

A numeric matrix or data.frame with equals numbers of rows and columns representing distances between objects to group.

suggested.k

It is an optional argument. A numeric value representing the suggested number of k-nearest neighbors to consider during the generating the kNN graph. Note that, due to the algorithm operation, this number may be different during the algorithm execution.

Author

Mario Inostroza-Ponta, Jorge Parraga-Alava, Pablo Moscato

Details

To see more details of how MST-kNN works refers to the quick guide.

References

Inostroza-Ponta, M. (2008). An Integrated and Scalable Approach Based on Combinatorial Optimization Techniques for the Analysis of Microarray Data. Ph.D. thesis, School of Electrical Engineering and Computer Science. University of Newcastle.

Examples

set.seed(1987)

##load package
library("mstknnclust")

##Generates a data matrix of dimension 100X15

n=100; m=15

x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m)

##Computes a distance matrix of x.

library("stats")
d <- base::as.matrix(stats::dist(x, method="euclidean"))

##Performs MST-kNN clustering using euclidean distance.

results <- mst.knn(d)

## Visualizes the clustering solution

library("igraph")
plot(results$network, vertex.size=8,
     vertex.color=igraph::clusters(results$network)$membership,
     layout=igraph::layout.fruchterman.reingold(results$network, niter=10000),
     main=paste("MST-kNN \n Clustering solution \n Number of clusters=",results$cnumber,sep="" ))