(This article was first published on

**One Tip Per Day**, and kindly contributed to R-bloggers)If you want more info about clustering, I have another post about "Clustering analysis and its implementation in R". Here is the link:

http://onetipperday.blogspot.com/2012/04/clustering-analysis-2.html------------

Several R functions in this topic:

1.

**dist**(X) -- calculate the distance of rows of data matrix X. The default distance method is euclidean. It can be maximal, manhattan, binary etc.

> a=matrix(sample(9),nrow=3)

> a

[,1] [,2] [,3]

[1,] 5 2 9

[2,] 8 7 1

[3,] 6 4 3

> dist(a, diag=T, method='max')

1 2 3

1 0

2 8 0

3 6 3 0

> dist(a, diag=T, method='euc')

1 2 3

1 0.000000

2 9.899495 0.000000

3 6.403124 4.123106 0.000000

2.

**hclust**(D) -- hierarchical clustering of a distance/dissimilarity matrix (e.g output of dist function): join two most similar objects (based on similarity method) each time until there is one single cluster.hclust(D) can be displayed in a tree format, using plot(hclust(D)), or plclust(hclust(D))

3.

**heat****map**(X, distfun = dist, hclustfun = hclust, ...) -- display matrix of X and cluster rows/columns by distance and clustering method.One enhanced version is heatmap.2, which has more functions. For example, you can use

- key, symkey etc. for legend,
- "col=heat.colors(16)" or "col='greenred', breaks=16" to specify colors of image
- cellnote (text matrix with same dim), notecex, notecol for text in grid
- colsep/rowsep to define blocks of separation, e.g. colsep=c(1,3,6,8) will display a white separator at columns of 1, 3, 6, 8 etc.

Both have 'ColSideColors/RowSideColors', a color vector with length of cols/rows. Here is an example(http://chromium.liacs.nl/R_users/20060207/Renee_graphs_and_others.pdf).

Another enhanced version is pheatmap, which produced pretty heatmap with additional options:

Another enhanced version is pheatmap, which produced pretty heatmap with additional options:

- cellwidth/cellheight to set the size of cell
- treeheight_row/treeheight_col: height of tree
- annotation: a data.frame, each column is an annotation of columns of X. So, nrow(annotation)==ncol(X)
- legend/annotation_legend: whether to show legend
- filename: save to file

4.

**kmeans**(X, centers=k) -- partition points (actually rows of X matrix) into k clusters . For example:# a 2-dimensional example

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")

(cl <- kmeans(x, 2))

plot(x, col = cl$cluster)

points(cl$centers, col = 1:2, pch = 8, cex=2)

The number of cluster can be determined by plot of sum of squares, eg.

`# Determine number of clusters`

wss <- (nrow(x)-1)*sum(apply(x,2,var))

for (i in 2:20) wss[i] <- sum(kmeans(x,centers=i)$withinss)

plot(1:20, wss, type="b", xlab="Number of Clusters",ylab="Within groups sum of squares")

Using hclust and cutree can also set the number of clusters:

hc <- hclust(dist(x), "ward")

plot(hc) # the plot can also help to decide the # of clusters

memb <- cutree(hc, k = 2)

Note: kmean is using partition method to cluster, while hclust is to use hierarchical clustering method. Here is a series of nice lectures for this. A more detail for cluster can be found here: CRAN Task View: Cluster Analysis

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