I have performed a clustering analysis and I am attempting to choose the optimal number of clusters for my data. I've calculated the gap statistic (using clusGap() in R) and the statistic does not have a local maximum over the # of clusters I examined (k = 1-20). See the plot of the gap statistic below. For my purposes, it is intractable to have a large number of clusters. (I am analyzing hillslope cross-sectional elevation data to identify geomorphic archetypes of hillslopes, but it wouldn't really be useful to have 20+ archetypes to discuss.) So, given that my data do not have a readily apparent "optimum" number of clusters, what are some possible approaches to choosing the number of clusters my analysis will use in the end? Is it appropriate to pick the value that occurs at the plateau around k=9? Is this indicative of there being excessive within-cluster variability, perhaps making this approach not very useful?
EDIT: To clarify, the data that I am clustering is a pairwise distance matrix of hillslope shapes, using dynamic time warping distance as the measure.