# and curiously, four clusters is not in the output at all! # Looks like 3 is the most frequently determined number of clusters Hist(nb$Best.nc, breaks = max(na.omit(nb$Best.nc))) Nb <- NbClust(d, diss=NULL, distance = "euclidean", The NbClust package provides 30 indices to determine the number of clusters in a dataset. You may also find it useful to explore your data with clustergrams to visualize cluster assignment, see for more details.Įight. Here's the output from Edwin Chen's implementation of the gap statistic: unclear to me): n = 100ĭ Number of clusters (method 'firstSEmax', SE.factor=1): 4 The wikipedia article on determining numbers of clusters has a good review of some of these methods.įirst, some reproducible data (the data in the Q are. If your question is " how can I determine how many clusters are appropriate for a kmeans analysis of my data?", then here are some options.
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