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## demo of k-means clustering... | |
## Step 1: make up some data | |
x <- rbind( | |
matrix(rnorm(100, mean=0, sd = 0.3), ncol = 2), # cluster 1 | |
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2), # cluster 2 | |
matrix(c(rnorm(50, mean = 1, sd = 0.3), # cluster 3 | |
rnorm(50, mean = 0, sd = 0.3)), ncol = 2)) | |
colnames(x) <- c("x", "y") | |
## Step 2: show the data without clustering | |
plot(x) | |
## Step 3: show the data with the known clusters (this is just so we | |
## can see how well k-means clustering recreates the original clusters we | |
## created in step 1) | |
colors <- as.factor(c( | |
rep("c1", times=50), | |
rep("c2", times=50), | |
rep("c3", times=50))) | |
plot(x, col=colors) | |
## Step 3: cluster the data | |
## NOTE: nstart=25, so kmeans() will cluster using 25 different starting points | |
## and return the best cluster. | |
(cl <- kmeans(x, centers=3, nstart=25)) | |
## Step 4: plot the data, coloring the points with the clusters | |
plot(x, col = cl$cluster) |