animation (version 2.0-5)

kmeans.ani: Demonstration of the k-Means clustering algorithm

Description

This function provides a demo of the k-Means cluster algorithm for data containing only two variables (columns).

Usage

kmeans.ani(x = cbind(X1 = runif(50), X2 = runif(50)), 
    centers = 3, pch = 1:3, col = 1:3, hints = c("Move centers!", 
        "Find cluster?"))

Arguments

x

A numercal matrix or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns) containing only 2 columns.

centers

Either the number of clusters or a set of initial (distinct) cluster centres. If a number, a random set of (distinct) rows in x is chosen as the initial centres.

pch,col

Symbols and colors for different clusters; the length of these two arguments should be equal to the number of clusters, or they will be recycled.

hints

Two text strings indicating the steps of k-means clustering: move the center or find the cluster membership?

Value

• A list with components
• cluster
  A vector of integers indicating the cluster to which each point is allocated.
• centers
  A matrix of cluster centers.

Details

The k-Means cluster algorithm may be regarded as a series of iterations of: finding cluster centers, computing distances between sample points, and redefining cluster membership.

The data given by x is clustered by the $k$-means method, which aims to partition the points into $k$ groups such that the sum of squares from points to the assigned cluster centers is minimized. At the minimum, all cluster centres are at the mean of their Voronoi sets (the set of data points which are nearest to the cluster centre).

References

http://animation.yihui.name/mvstat:k-means_cluster_algorithm

See Also

kmeans

Examples

## set larger 'interval' if the speed is too fast
oopt = ani.options(interval = 2)
par(mar = c(3, 3, 1, 1.5), mgp = c(1.5, 0.5, 0))
kmeans.ani()

## the kmeans() example; very fast to converge!
x = rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), 
    matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) = c("x", "y")
kmeans.ani(x, centers = 2)

## what if we cluster them into 3 groups?
kmeans.ani(x, centers = 3)

## create an HTML animation page
saveHTML({
    ani.options(interval = 2)
    par(mar = c(3, 3, 1, 1.5), mgp = c(1.5, 0.5, 0))
    
    cent = 1.5 * c(1, 1, -1, -1, 1, -1, 1, -1)
    x = NULL
    for (i in 1:8) x = c(x, rnorm(25, mean = cent[i]))
    x = matrix(x, ncol = 2)
    colnames(x) = c("X1", "X2")
    
    kmeans.ani(x, centers = 4, pch = 1:4, col = 1:4)
    
}, img.name = "kmeans.ani", htmlfile = "kmeans.ani.html", ani.height = 480, 
    ani.width = 480, title = "Demonstration of the K-means Cluster Algorithm", 
    description = "Move! Average! Cluster! Move! Average! Cluster! ...")

ani.options(oopt)