kmed (version 0.1.0)

stepkmed: Step k-medoid algorithm from Yu et al.

Description

This function computes and returns the clustering result computed by using a specified distance via Yu et al. algorithm.

Usage

stepkmed(distdata, ncluster, iterate = 10, alpha = 1)

Arguments

distdata

A matrix of distance objects (n x n) or a diss class.

ncluster

A number of cluster.

iterate

A number of iteration for clustering algorithm.

alpha

A numeric number to determine the range of initial medoids selection.

Value

Function returns a partitioning clustering algorithm result consists of cluster membership, cluster medoid, the minimum distance to the cluster medoid.

Details

This is a k-medoids algorithm that has been proposed by Yu et al. The algorihm has been claimed to be a remedy of simple and fast k-medoid. The medoids updating in this algorihm is similar to simple and fast k-medoid.

References

Yu, D., Liu, G., Guo, M., Liu, X., 2018. An improved K-medoids algorithm based on step increasing and optimizing medoids. Expert Systems with Applications 92, 464-473.

Examples

# NOT RUN {
num <- as.matrix(iris[,1:4])
mrwdist <- distNumeric(num, num, method = "mrw")
result <- stepkmed(mrwdist, ncluster = 3, iterate = 50, alpha = 1.5)
table(result$cluster, iris[,5])


# }