# clustering: Agglomerative hierarchical clustering In EMA: Easy Microarray Data Analysis

## Description

Computes agglomerative hierarchical clustering of the dataset.

## Usage

 `1` ``` clustering(data, metric="euclidean", method="ward", nb) ```

## Arguments

 `data` Expression matrix, genes on rows and samples on columns `metric` Character string specifying the metric to be used for calculating dissimilarities between the columns of the matrix. This must be one of 'euclidean', 'manhattan', 'pearson', 'pearsonabs', 'spearman', 'spearmanabs', 'jaccard', 'dice' `method` Character string defining the clustering method. This must be one of 'average', 'single', 'complete', 'ward' `nb` The number of classes for kmeans and PAM clustering (kcentroids)

## Details

Available metrics are (written for two vectors x and y):

euclidean:

Usual square distance between the two vectors.

manhattan:

Absolute distance between the two vectors

pearson:

Pearson correlation distance. (1 - r)/2

pearsonabs:

Absolute Pearson correlation distance. 1 - abs(r)

spearman:

Spearman rank correlation distance. (1 - r)/2

spearmanabs:

Absolute Spearlan rnak correlation distance. 1 - abs(r)

jaccard:

Jaccard distance on 0-1 matrix

dice:

Dice distance on 0-1 matrix

Available agglomerative methods are :

average:

The distance between two clusters is the average of the dissimilarities between the points in one cluster and the points in the other cluster.

single:

we use the smallest dissimilarity between a point in the first cluster and a point in the second cluster (nearest neighbor method).

complete:

we use the largest dissimilarity between a point in the first cluster and a point in the second cluster

ward:

Ward's agglomerative method

weighted:

The weighted distance from the agnes package

diana:

computes a divise clustering

kcentroids:

Perform either kmeans clustering if the distance is euclidean or PAM clustering. The number of classes nb has to be done.

## Value

An object of class 'agnes' representing the clustering. See 'agnes.object' for details.

## Author(s)

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

## References

Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

`agnes`,`clust.dist`
 ```1 2 3``` ```data(marty) c<-clustering(marty, metric="pearson", method="ward") clustering.plot(c, title="Hierarchical Clustering\nPearson-Ward") ```