Is there a built-in function for data clustering using k-medians algorithm? I found out about the ClusterDissimilarityFunction of the FindCLusters function, but I guess it refers to the distance function (mean for euclidean, median for manhattan etc). So my kmeans using manhattan metric looks like this:

FindClusters[Transpose[{height, weight}], 2, 
 Method -> {"KMeans", ClusterDissimilarityFunction -> "Median", 
   "InitialCentroids" -> {{0, 0}, {100, 100}}}] 

Is it correct? How to compute k-medians?

  • 4
    $\begingroup$ If it can't be done with the built-in functions, you could take the implementation of k-means from here and replace Mean with Median. That should do it. $\endgroup$ – C. E. Jan 12 '19 at 22:54

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