fastkmed: Simple and fast k-medoid algorithm

In kmed: Distance-Based k-Medoids

Simple and fast k-medoid algorithm

Description

This function runs the simple and fast k-medoid algorithm proposed by Park and Jun (2009).

Usage

fastkmed(distdata, ncluster, iterate = 10, init = NULL)

Arguments

distdata	A distance matrix (n x n) or dist object.
ncluster	A number of clusters.
iterate	A number of iterations for the clustering algorithm.
init	A vector of initial objects as the cluster medoids (see Details).

Details

The simple and fast k-medoids, which sets a set of medoids as the cluster centers, adapts the k-means algorithm for medoid up-dating. The new medoids of each iteration are calculated in the within cluster only such that it gains speed.

init = NULL is required because the Park and Jun (2009) has a particular method to select the initial medoids. The initial medoids are selected by

v_j = ∑_{i=1}^n \frac{d_{ij}}{∑_{l=1}^n d_{il}}, \quad j = 1, 2, 3, …, n

where the first k of the v_j is selected if the number of clusters is k.

init can be provided with a vector of id objects. The length of the vector has to be equal to the number of clusters. However, assigning a vector in the init argument, the algorithm is no longer the simple and fast k-medoids algorithm. The inckmed function, for example, defines a different method to select the initial medoid though it applies the fastkmed function.

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: [e-mail]

References

Park, H., Jun, C., 2009. A simple and fast algorithm for k-medoids clustering. Expert Systems with Applications 36, pp. 3336-3341.

Examples

num <- as.matrix(iris[,1:4])
mrwdist <- distNumeric(num, num, method = "mrw")
result <- fastkmed(mrwdist, ncluster = 3, iterate = 50)
table(result$cluster, iris[,5])

kmed documentation built on Aug. 29, 2022, 9:06 a.m.