Computes agglomerative hierarchical clustering of the dataset.
agnes(x, diss = inherits(x, "dist"), metric = "euclidean", stand = FALSE, method = "average")
- data matrix or data frame, or dissimilarity matrix, depending on the
value of the
In case of a matrix or data frame, each row corresponds to an observation, and each column corresponds to a variable. All variables
- logical flag: if TRUE (default for
xis assumed to be a dissimilarity matrix. If FALSE, then
xis treated as a matrix of observations by variables.
- character string specifying the metric to be used for calculating dissimilarities between observations. The currently available options are "euclidean" and "manhattan". Euclidean distances are root sum-of-squares of differences, and manhat
- logical flag: if TRUE, then the measurements in
xare standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the vari
- character string defining the clustering method. The five methods implemented are "average" (group average method), "single" (single linkage), "complete" (complete linkage), "ward" (Ward's method), and "weighted" (weighted average linkage).
agnes is fully described in chapter 5 of Kaufman and Rousseeuw (1990).
Compared to other agglomerative clustering methods such as
agnes has the following features: (a) it yields the
agglomerative coefficient (see
which measures the amount of clustering structure found; and (b)
apart from the usual tree it also provides the banner, a novel
graphical display (see
agnes-algorithm constructs a hierarchy of clusterings.
At first, each observation is a small cluster by itself. Clusters are
merged until only one large cluster remains which contains all the
observations. At each stage the two nearest clusters are combined
to form one larger cluster.
method="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.
method="single", we use the smallest dissimilarity between a
point in the first cluster and a point in the second cluster (nearest
method="complete", we use the largest dissimilarity
between a point in the first cluster and a point in the second cluster
(furthest neighbor method).
- an object of class
"agnes"representing the clustering. See
Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996):
Clustering in an Object-Oriented Environment.
Journal of Statistical Software, 1.
Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, 26, 17--37.
data(votes.repub) agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE) agn1 plot(agn1) agn2 <- agnes(daisy(votes.repub), diss = TRUE, method = "complete") plot(agn2) data(agriculture) ## Plot similar to Figure 7 in ref plot(agnes(agriculture), ask = TRUE) <testonly>plot(agnes(agriculture))</testonly>