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,ChristophAm 01.03.2018 21:40 schrieb "Matt Hicks" <firstname.lastname@example.org>:
On Thu, Mar 1, 2018 2:36 PM, Christoph Brücke email@example.com 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?Best,ChristophAm 01.03.2018 20:53 schrieb "Matt Hicks" <firstname.lastname@example.org>:
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?