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    $\begingroup$ Thanks for this contribution, @user2341646. Would you mind adding some exposition that explains what this solution is, how it works, & why it is a solution? $\endgroup$ Commented Nov 23, 2013 at 16:51
  • $\begingroup$ OK. Basically, the algorithm starts with membership assignments that are random, but there are close to G members in a cluster, and there are K clusters overall. We define the error function that measures the average distances between data in one cluster, averaged over all clusters. Going through all pairs of data systematically, we see if exchanging the membership of those two data results in a lower error. If it does, we update the lowest possible error, otherwise we undo the membership switch. We do this until there are no more moves left for one entire pass. $\endgroup$ Commented Nov 27, 2013 at 19:00
  • $\begingroup$ Hey Alexander, sorry for resurrecting your answer, but do you have any paper for referencing purposes? $\endgroup$ Commented Mar 2, 2021 at 17:50