EMA (version 1.4.3)

clustering: Agglomerative hierarchical clustering

Description

Computes agglomerative hierarchical clustering of the dataset.

Usage

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)

Value

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

describe

• 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.

Details

Available metrics are (written for two vectors x and y): [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

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

See Also

agnes,clust.dist

Examples

data(marty)
c<-clustering(marty, metric="pearson", method="ward")
clustering.plot(c, title="Hierarchical Clustering
Pearson-Ward")