One approach that's often used is to calculate the between to within mean square ratio (as in a one-way ANOVA), then calculate the F ratio and p-value for each cut, and then use the cut with the smallest p-value. This is at best a useful heuristic, however-- probably the best approach is to choose the clustering that is most interpretable substantively, or to use a technique such as K-means designed to find a simple clustering (or partition) in the first place! (K-means explicitly maximizes the F ratio discussed above for a partitioning into K clusters, so you may want to use the relative p-values as well as interpretability and other criteria to choose the appropriate value of K.) Doug Carroll At 12:49 PM 5/5/2004 -0500, you wrote: >Dear all, > >I am now working on the hierarchical clustering methods, and >confused about the following problem. > >As you know, to form clustering from the hierarchical tree generated by >the pairwise distance bw the elements, we have to set a threshold value >to cut the tree horizonally such that the vertical links intersecting with >this horizonal critical value will be the final clusters. > >However, I do not find a very robust criterion for choosing the >optimal number of clusters or calculating this threshold value to make the >clustering results good different pairwise distance(similairty) measure. > >So any one has some point on this problem or recommended papers >or methods? > >Thanks for your help. > >Fred > > > ###################################################################### # J. Douglas Carroll, Board of Governors Professor of Management and # #Psychology, Rutgers University, Graduate School of Management, # #Marketing Dept., MEC125, 111 Washington Street, Newark, New Jersey # #07102-3027. Tel.: (973) 353-5814, Fax: (973) 353-5376. # # Home: 14 Forest Drive, Warren, New Jersey 07059-5802. # # Home Phone: (908) 753-6441 or 753-1620, Home Fax: (908) 757-1086. # # E-mail: [log in to unmask] # ######################################################################