cluster (version 1.2-1)

pam: Partitioning Around Medoids

Description

Returns a list representing a clustering of the data into k clusters.

Usage

pam(x, k, diss = F, metric = "euclidean", stand = F)

Arguments

x

data matrix or dataframe, or dissimilarity matrix, depending on the value of the diss argument.

In case of a matrix or dataframe, each row corresponds to an observation, and each column corresponds to a variable. All variables must be numer

k

integer, the number of clusters. It is required that 0 < k < n where n is the number of observations.

diss

logical flag: if TRUE, then x will be considered as a dissimilarity matrix. If FALSE, then x will be considered as a matrix of observations by variables.

metric

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 manhattan distances ar

stand

logical flag: if TRUE, then the measurements in x are standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean

Value

• an object of class "pam" representing the clustering. See pam.object for details.

BACKGROUND

Cluster analysis divides a dataset into groups (clusters) of observations that are similar to each other. Partitioning methods like pam, clara, and fanny require that the number of clusters be given by the user. Hierarchical methods like agnes, diana, and mona construct a hierarchy of clusterings, with the number of clusters ranging from one to the number of observations.

NOTE

For datasets larger than (say) 200 observations, pam will take a lot of computation time. Then the function clara is preferable.

Details

pam is fully described in chapter 2 of Kaufman and Rousseeuw (1990). Compared to the k-means approach in kmeans, the function pam has the following features: (a) it also accepts a dissimilarity matrix; (b) it is more robust because it minimizes a sum of dissimilarities instead of a sum of squared euclidean distances; (c) it provides a novel graphical display, the silhouette plot (see plot.partition) which also allows to select the number of clusters.

The pam-algorithm is based on the search for k representative objects or medoids among the observations of the dataset. These observations should represent the structure of the data. After finding a set of k medoids, k clusters are constructed by assigning each observation to the nearest medoid. The goal is to find k representative objects which minimize the sum of the dissimilarities of the observations to their closest representative object. The algorithm first looks for a good initial set of medoids (this is called the BUILD phase). Then it finds a local minimum for the objective function, that is, a solution such that there is no single switch of an observation with a medoid that will decrease the objective (this is called the SWAP phase).

References

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. http://www.stat.ucla.edu/journals/jss/

Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, 26, 17--37.

See Also

pam.object, clara, daisy, partition.object, plot.partition, dist.

Examples

# generate 25 objects, divided into 2 clusters.
x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
           cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
pamx <- pam(x, 2)
pamx
summary(pamx)
plot(pamx)

pam(daisy(x, metric = "manhattan"), 2, diss = T)

data(ruspini)
## Plot similar to Figure 4 in Stryuf et al (1996)
plot(pam(ruspini, 4), ask = TRUE)
<testonly>plot(pam(ruspini, 4))</testonly>