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In sklearn's description of the silhouette_score method, it says that negative values stand for data points that are wrongly assigned to a cluster. I am wondering, how this is possible for the k-means algorithm for which each data point is assigned to nearest cluster, so lowest distance. If this is done then how can we find negative silhouette-scores? Is this only possible under non-equally weighting of different objects?

Thanks in advance!

Ralf
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    Please see my answer to a similar question: https://stackoverflow.com/a/66751204/4542084 Note that a negative silhouette score for a point does not necessarily mean a "wrong" assignment, it just means that the point is on average closer to points in another cluster than to points in its own cluster. Depending on the structure of the data, this may have multiple interpretations. – Burrito Mar 22 '21 at 17:53

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