# hclust analyse methods, R [duplicate]

I use currently the function hclust() for Dendogram in R. It looks like:

res.hc <- hclust(d, method = "ward.D2" )


My special interest is to understand, what the method I have to use for my data and where is a difference. I already took a look to the R Documentation ?hclust

The documentation is very poor, I could find only this part:

A number of different clustering methods are provided. Ward's minimum variance method aims at finding compact, spherical clusters. The complete linkage method finds similar clusters. The single linkage method (which is closely related to the minimal spanning tree) adopts a ‘friends of friends’ clustering strategy. The other methods can be regarded as aiming for clusters with characteristics somewhere between the single and complete link methods. Note however, that methods "median" and "centroid" are not leading to a monotone distance measure, or equivalently the resulting dendrograms can have so called inversions or reversals which are hard to interpret, but note the trichotomies in Legendre and Legendre (2012).

Two different algorithms are found in the literature for Ward clustering. The one used by option "ward.D" (equivalent to the only Ward option "ward" in R versions <= 3.0.3) does not implement Ward's (1963) clustering criterion, whereas option "ward.D2" implements that criterion (Murtagh and Legendre 2014). With the latter, the dissimilarities are squared before cluster updating. Note that agnes(, method="ward") corresponds to hclust(, "ward.D2").

Can anybody provide me better introduction to this method and probably try to explain, how I can select the right method for my data (my data is very similar to ibis setosa dataset).