Minimizing area difference between concave hull and convex hull

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Suppose I have multiple n-dimensional data points. The area difference between its concave hull and convex hull is larger than a certain desired threshold. Is there an algorithm that can cluster the data points to m clusters so that the sum of area difference between its concave hull and convex hull for each cluster is minimized to be lower than a certain threshold, but at the same time keeping the cluster count m as small as possible?