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I've recently run a very large data set through a multidimensional scaling analysis and am attempting to cluster the results into groups. I've read a few papers that utilize hierarchical clustering to accomplish this, however my data set is still rather large which is making Hclustering unfeasible. I have instead decided to use k-means, but I have yet to find any articles that either support this method or have used it.

Is k-means clustering a feasible option for my objective?

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Yes and no. There are some procedures that work in this way: you fix k=100. You run such a k-means, then you take the groups and mount a hierarchy. I found this method implemented in an old software called SPAD.

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  • $\begingroup$ Could you elaborate a little more on that process? To my understanding what you are saying is that I should run a k-means with a fixed k, then run those clusters through a hierarchical model? $\endgroup$
    – r_user
    Feb 25, 2019 at 12:43
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    $\begingroup$ It is a hybrid between k-means and hierarchical clustering. Once you obtain a k(let's say k=100), you have 100 clusters. With these 100 groups, you decide what linkage (complete, average, minimum, Ward, etc.) you want and mount a hierchy starting from the 100 clusters. $\endgroup$
    – antonio irpino
    Mar 22, 2019 at 10:15

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