K-means clustering

K-means is the process of assigning objects to groups so that the sum of the squares of the groups is minimized. R has the kmeans function available for cluster analysis. K-means is a method of determining clusters based on partitioning the data and assigning items in the dataset to the nearest cluster.

K-means clustering is done in R using the kmeans function. The kmeans function is defined as follows:

kmeans(x, centers, iter.max = 10, nstart = 1,
   algorithm = c("Hartigan-Wong", "Lloyd", "Forgy","MacQueen"), trace=FALSE)

The various parameters of this function are described in the following table:

Parameter	Description
x	This is the dataset.
centers	This contains the number of centers/clusters to find.
iter.max	This stores the maximum number of iterations allowed.
nstart	This contains the number of random clusters to find.
algorithm	This contains the algorithm to be used to determine clusters. Hartigan-Wong is the default. Lloyd and Forgy are the same...