I am trying to get NeighborFinder to match effectively two different datasets in such as why that each candidate is only matched to one base but not all bases have candidates. (Should be able to find these by utilizing the Maximum Distance Field). However, when the data gets chaotic and there are 10 measurements on top of each other, the matching candidates (which are labels) are often not the closest candidates. I am wondering if anyone has an idea about how to match a cluster of measurements with their candidates in such as way that it produces the lowest average distance between the bases and candidates and makes sure that each candidate finds a reasonably close base. The post: https://knowledge.safe.com/questions/59605/neighborfinder-and-excluding-candidates-after-matc.html gives me an idea, but I do not think that it provides a complete answer to my problem.
This image illustrates the problem. I bet that using a waterfall technique would get about 20% of this wrong.
Best answer by jdh
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