ClusterEvaluator: Intra-cluster density calculation fails for cluster containing almost identical points

[Derek O'Callaghan <de...@ucd.ie>]

Hi Jeff,

I've been trying out the ClusterEvaluator class today since your recent 
changes, and I'm running into a problem whereby the average 
intra-cluster density can be set to NaN. Looking into it, it seems to 
happen for clusters containing points which are very close to the 
centroid.  For example, I have a cluster with:

Centroid:

{0:0.6075199543688895,1:-0.3165058387409551,2:0.2027106147825682,3:-21.246338574215706,4:-5.875047828899212,5:-0.9835694086952028,6:0.2794019939470805,7:-0.36402079609289717,8:0.5201946127074457,9:-0.47084217746293855,10:-0.14380397719670499,11:-0.10441028152861193,12:0.0698485086335405,13:0.014286758874801297}

and one of the representative points (3 per cluster):

[0.6075199543688894, -0.31650583874095506, 0.2027106147825682, 
-21.2463385742157, -5.875047828899212, -0.9835694086952026, 
0.27940199394708054, -0.36402079609289706, 0.5201946127074457, 
-0.47084217746293855, -0.14380397719670499, -0.10441028152861194, 
0.06984850863354047, 0.014286758874801297]

As far as I can tell from debugging, the representative points look 
identical to the centroid of this cluster, but I'm assuming there's some 
small difference as "if (!vector.equals(clusterI.getCenter()))" in 
ClusterEvaluator.invalidCluster() is always returning false for these 
points, and so the cluster isn't pruned from the list.

Later on, in ClusterEvaluator.intraClusterDensity(), the "min" and "max" 
distances are ending up with the same value, and the density from 
"double density = (sum / count - min) / (max - min);" is calculated as 
NaN, e.g. here are the values I'm getting:

min = max = 1.5397509610616733E-7
count = 3
sum = 4.61925288318502E-7
max - min: 0.0
count - min: 2.9999998460249038
(sum / count - min) = 0.0

This then causes avgDensity to be calculated as NaN. I'm not sure what 
the solution is here, should invalidCluster() check that the the 
difference between the centroid and the candidate representative point 
is greater than a certain threshold, which would cause such a cluster to 
be pruned? Or is the fix in the intraClusterDensity() calculation to 
handle the case where min = max?

BTW would you prefer that I create a Jira to record these issues, or is 
it okay to send them to the dev list as I've been doing?

Thanks,

Derek