Thanks for the replies guys!
@Aljoscha: I get your point, but I would actually expect either an error
message or the lonely centroid to move.
k-means is supposed to cluster data in k clusters. If you end up with < k,
something must have gone wrong.. :s
@Alex: very helpful resource, thanks. I will
Apache's commons-math implementation offers various strategies for handling
this scenarios:
http://commons.apache.org/proper/commons-math/jacoco/org.apache.commons.math3.stat.clustering/KMeansPlusPlusClusterer.java.html
(take a look at the EmptyClusterStrategy enum options)
2015-02-24 23:28 GMT+
I think the behaviour is correct. If a cluster has not points then it
has no centroid. If it has no centroid no points could ever be
assigned to it again in the future since there is no way of
calculating a distance.
On Tue, Feb 24, 2015 at 6:57 PM, Vasiliki Kalavri
wrote:
> Hello everyone,
>
> I
Hello everyone,
I'm using the k-means example as basis for a custom implementation and I
noticed the following behavior: If during an iteration no point is assigned
to a particular cluster, this cluster will then "disappear".
This happens because SelectNearestCenter() outputs
tuples, (where centr
