| skm {kmed} | R Documentation |
Simple k-medoid algorithm
Description
This function runs the simple k-medoid algorithm proposed by Budiaji and Leisch (2019).
Usage
skm(distdata, ncluster, seeding = 20, iterate = 10)
Arguments
distdata |
A distance matrix (n x n) or dist object. |
ncluster |
A number of clusters. |
seeding |
A number of seedings to run the algorithm (see Details). |
iterate |
A number of iterations for each seeding (see Details). |
Details
The simple k-medoids, which sets a set of medoids as the cluster centers, adapts the simple and fast k-medoid algoritm. The best practice to run the simple and fast k-medoid is by running the algorithm several times with different random seeding options.
Value
Function returns a list of components:
cluster is the clustering memberships result.
medoid is the id medoids.
minimum_distance is the distance of all objects to their cluster
medoid.
Author(s)
Weksi Budiaji
Contact: budiaji@untirta.ac.id
References
W. Budiaji, and F. Leisch. 2019. Simple K-Medoids Partitioning Algorithm for Mixed Variable Data. Algorithms Vol 12(9) 177
Examples
num <- as.matrix(iris[,1:4])
mrwdist <- distNumeric(num, num, method = "mrw")
result <- skm(mrwdist, ncluster = 3, seeding = 50)
table(result$cluster, iris[,5])