# Is there an advantage to squaring dissimilarities when using Ward clustering?

Is there a reason to prefer squaring or not squaring the dissimilarities when clustering with Ward's method?

The question is motivated by the following statement in the documentation for R's hclust() function:

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 2013). With the latter, the dissimilarities are squared before cluster updating.

Does squaring improve the algorithm?

• Uhm. Unless you show the results of the two methods, along with the input matrix, that question would look much as purely R question. – ttnphns Apr 30 '14 at 13:31

For example, hclust(dist(x)^2,method="ward") is equivalent to hclust(dist(x),method="ward.D2").
Judging from the explanation, ward in R was first implemented incorrectly.
Only in recent versions, a corrected version of ward linkage was added, as ward.D2. So if you want to use ward linkage, use ward.D2.