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If your definition of points belonging to a cluster is simply the points closest the cluster centroid, then the boundaries cannot overlap. The point assignments are a Voronoi map like: (Source: https://www.quora.com/What-is-the-difference-between-K-Means-and-Voronoi/answer/Ethan-Brooks-3) The closest centroid, and thus assignment, is unambiguous.


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A lot of the values from your data seem to be of the same size, or very small difference, also it seems it is important to deal with noise. The same problem is true for image compression. I think a good approach would be to use Haar-Wavelet Transform which compresses the information by a very huge amount and reduces the noisy data, like it does for image ...


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