cluster analysis - maxmin clustering algorithm -

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I have read a paper that mentions the maximum minimum clustering algorithm, but I do not understand this completely What does the algorithm do? Googling "maximal clustering algorithm" does not yield any helpful results Does anyone know what this algorithm is? This is a fraction of the paper:

By selecting an observation as the first nucleus C1 and setting the centroids to C {c1}, the maximum-min clustering income During the ith iteration, CI is chosen that it maximizes the minimum Euclidean distance between C and observes in C . Max-min clustering is better for a density-based clustering algorithm (such as a key), which is used to select several examples of dense group of non-seizure data points.

I do not quite understand the adventurous part paper link

We There is probably some python code here with existing centrosides for each new center. 

  def maxminclustering (comments, k): comments = set (comments) if k & lt; 1 or not observation: return set () centroids = set for category (min (k-1, lane (observation)): newcentroid = max (comments, key = lambda overview : Minutes for centroid in centroids (distance (observation, centroid))) comments. Away (newcentroid) centroids.add (newcentroid) returns centroids

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