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Segmentation: K-Mean or agglomerative hierarchical clustering


Would please explain the difference between application of  K-mean and agglomerative hierarchical clustering? In which study context each of them is more useful or efficient?
( Assume we use CBC/HB)
asked Aug 16, 2017 by Robin59 Bronze (545 points)

1 Answer

0 votes

K-means and agglomerative hierarchical clustering are just two algorithms for developing clusters  of records with "similar" profiles, at least when you have no dependent variable to use to define similarity (i.e. when you're doing "unsupervised partitioning").  Divisive hierarchical clustering, K-medians and cluster ensembles are examples of other ways of tackling the same problem.  I typically use a variety of these methods when I'm creating segments because no one of them consistently outperforms the others (though I end up using the k-means, k-medians or cluster ensembles solutions the most often).  

With large sample sizes k-means will run faster (so in that sense it's more efficient) but I really can't tell you that one of these is reliably more useful than all the others.
answered Aug 16, 2017 by Keith Chrzan Platinum Sawtooth Software, Inc. (115,950 points)
Hi, Keith,

have you ever looked at the differences between the result "Mixed Logit (HB) + Clustering (e.g. k-means)" and "Latent Class Mixed Logit (HB)"? The latter is not implemented in Lighthouse Studio, but I would be interested in the differences.

Best wishes