Thanks Alessandro and Christoph.  I appreciate the feedback, but I'm still
having issues determining how to actually accomplish this with the API.
Can anyone point me to an example in code showing how to accomplish this?  

On Fri, Mar 2, 2018 2:37 AM, Alessandro Solimando 
Hi Matt,similarly to what Christoph does, I first derive the cluster id for the
elements of my original dataset, and then I use a classification algorithm
(cluster ids being the classes here).
For this method to be useful you need a "human-readable" model, tree-based
models are generally a good choice (e.g., Decision Tree).

However, since those models tend to be verbose, you still need a way to
summarize them to facilitate readability (there must be some literature on this
topic, although I have no pointers to provide).

On 1 March 2018 at 21:59, Christoph Brücke <>  wrote:
Hi Matt,
I see. You could use the trained model to predict the cluster id for each
training point. Now you should be able to create a dataset with your original
input data and the associated cluster id for each data point in the input data.
Now you can group this dataset by cluster id and aggregate over the original 5
features. E.g., get the mean for numerical data or the value that occurs the
most for categorical data.
The exact aggregation is use-case dependent.
I hope this helps,Christoph

Am 01.03.2018 21:40 schrieb "Matt Hicks" <>:
Thanks for the response Christoph.
I'm converting large amounts of data into clustering training and I'm just
having a hard time reasoning about reversing the clusters (in code) back to the
original format to properly understand the dominant values in each cluster.
For example, if I have five fields of data and I've trained ten clusters of data
I'd like to output the five fields of data as represented by each of the ten

On Thu, Mar 1, 2018 2:36 PM, Christoph Brücke  wrote:
Hi matt,
the cluster are defined by there centroids / cluster centers. All the points
belonging to a certain cluster are closer to its than to the centroids of any
other cluster.
What I typically do is to convert the cluster centers back to the original input
format or of that is not possible use the point nearest to the cluster center
and use this as a representation of the whole cluster.
Can you be a little bit more specific about your use-case?
Am 01.03.2018 20:53 schrieb "Matt Hicks" <>:
I'm using K Means clustering for a project right now, and it's working very
well.  However, I'd like to determine from the clusters what information
distinctions define each cluster so I can explain the "reasons" data fits into a
specific cluster.
Is there a proper way to do this in Spark ML?

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