Hierarchical clustering on both disimilarities and data
Usage
Rclusterpp.hclust(x, method = "ward", members = NULL, distance = "euclidean", p = 2)
Arguments
x
A numeric data matrix, data frame or a dissimilarity structure as produced by dist.
method
The agglomeration method to be used. This must be one of "ward", "single", "complete" or "average".
members
NULL or a vector with length size of x. See hclust.
distance
The distance measure to be used. This must be one of "euclidiean", "manhattan", "maximum", or "minkowski".
p
The power of the Minkowski distance.
Value
An object of class *hclust* which describes the tree produced by the clustering process. See hclust.
Details
If x is a disimilarity matrix, execution defaults to standard hclust. If
x is a set of observations, specialized native clustering routines are
invoked. These routines are optimized for O(n) memory footprint and multicore
execution to permit clustering of large datasets.
References
Murtagh, F. (1983), "A survey of recent advances in hierarchical clustering algorithms", Computer Journal, 26, 354-359.
Sibson, R. (1973), "SLINK: An optimally efficient algorithm for the single-link cluster method", Computer Journal, 16, 30-34.