clusternor (version 0.0-2)

Kmedoids: Perform k-medoids clustering on a data matrix. After initialization the k-medoids algorithm partitions data by testing which data member of a cluster Ci may make a better candidate as medoid (centroid) by reducing the sum of distance (usually taxi), then running a reclustering step with updated medoids.

Description

Perform k-medoids clustering on a data matrix. After initialization the k-medoids algorithm partitions data by testing which data member of a cluster Ci may make a better candidate as medoid (centroid) by reducing the sum of distance (usually taxi), then running a reclustering step with updated medoids.

Usage

Kmedoids(data, centers, nrow = -1, ncol = -1,
  iter.max = .Machine$integer.max, nthread = -1, init = c("forgy"),
  tolerance = 1e-06, dist.type = c("taxi", "eucl", "cos"))

Arguments

data

Data file name on disk or In memory data matrix

centers

The number of centers (i.e., k)

nrow

The number of samples in the dataset

ncol

The number of features in the dataset

iter.max

The maximum number of iteration of k-means to perform

nthread

The number of parallel threads to run

init

The type of initialization to use c("forgy")

tolerance

The convergence tolerance

dist.type

What dissimilarity metric to use

Value

A list containing the attributes of the output of kmedoids. cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated. centers: A matrix of cluster centres. size: The number of points in each cluster. iter: The number of (outer) iterations.

Examples

# NOT RUN {
iris.mat <- as.matrix(iris[,1:4])
k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
km <- Kmedoids(iris.mat, k)

# }