Thank you for reading my question. I have an archaeological case-study, that we can call "Site1", that I want to compare with 9 others "Sites" studied by other scholars. For all of them I have 8 economic (indipendent) variables based on the frecuency of certain artifacts. How can I do to explore the 'economnic' variabiliy of this sites, and more specifically to see to which sites my case-study is more similar to?

This are the steps I followed:

  • I changed from frecuency to percentages in order to avoid the among sites variability. There are site with a lot of observations and sites with only twenty observations. But I am not interested in absolute frecuency, but in relative ones.

  • I calculated Z-values for the entire sample from the percentage values.

  • I run a HCA with Ward method, excluding my 'Site1' from the analysis. I obtain a three-cluster solution, with three Sites each one.

  • I save the cluster centers.

  • I run a K-means analysis using the Ward's cluster-centers, but this time including also my 'Site1'. As result, I see that the cluster-composition is not changed and that my 'Site1' is added to cluster2. So, I can run a One-way Anova and a Tukey to see which variables contribuite at most to each cluster.

Is it a right procedure? Would you suggest some different strategy?




Why don't you just assign the new observation to the nearest cluster center by least SSQ? Running k-means on a Ward clustering result is like doing a nice steak on the BBQ and then mincing it to make a frozen hamburger.

Technically, you use the cluster centers as new data set, then do a 1NN classification to classify your new observation.

  • $\begingroup$ It sound interesting, but no idea on how to perform an K-nearest neighbour... I don't have in my SPSS Statistic v22, and no idea on how do it with R. Why it is not correct to run a 'canonic' K-means? Just one more question... do you think it is right to peform Z-values on percentages? $\endgroup$ – Mark Jan 22 '15 at 20:39

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