I have a question regarding cluster analysis.

I was wondering if I have only 9 data points, is it valid to use k-means methods in cluster analysis?

I have done a special molecular evolution analysis and it has generated 9 points, what I want to know is that whether there is any clustering among the groups. What I did was to just assign numbers to 3 groups and then do ANOVA and post-hoc t-test, but I was wondering if there is a more appropriate method for that?


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    $\begingroup$ What data have you got about these nine points? $\endgroup$
    – Peter Flom
    Commented Dec 14, 2013 at 16:26
  • $\begingroup$ It's unclear to me what you mean by "points," "groups," and "numbers." You 'have' 9 data points; the analysis 'generated 9 points'; you are interested in 'clustering among the groups'; and you 'assigned numbers to 3 groups.' Clarification would probably bring you more answers. $\endgroup$
    – rolando2
    Commented Dec 15, 2013 at 4:46

1 Answer 1


For small data sets, hierarchical linkage clustering is a good choice.

Even single-linkage may do the trick for you.

You can afford to use the most expensive clustering algorithm (most linkage variants are in $O(n^3)$) if $n=8$; it's only for really large data sets where k-means shines because of its linear runtime.

Plus, linkage clustering allows you to use a domain specific similarity, measure, whereas k-means assumes your data to be Euclidean.


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