recent issue was needing high dimensionality clustering, which is relatively straightforward.
But I couldn't find one that would consistently group them the same so I could sequentially add data, which was frustrating, and the one I did find had a warning in it
"if you're on mac or linux, please increase the stack limit to unlimited, or it'll crash"
So I wrote it as a really basic pointwise k-means clustering approach, so it has a cost function that evaluates each point in a cluster against the centroid of each comparison cluster, and tries to boost the gaps between groups while minimising the gaps in each internal group.
And then I'm running it within a genetic algorithm to optimize the cost function.
and it's like 90% done after 2 days of work, which I am _hyped_ about