title: K-Means: Lloyd,Forgy,MacQueen,Hartigan-Wong
body: <p>I'm working with the K-Means Algorithm in R and I want to figure out the differences of the 4 Algorithms Lloyd,Forgy,MacQueen and Hartigan-Wong which are available for the function "kmeans" in the stats package.</p>
  
  <p>However I was notable to get a sufficient answer to this question.</p>
  
  <p>I only found some rarely information:
  (Visit <a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/K-Means">http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/K-Means</a>)</p>
  
  <p>From this description Lloyd, Forgy, and Hartigan-Wong seem the same to me. Minimizing the within sum of squares or Minimizing the Euclidean Distance is the same.</p>
  
  <p>MacQueen is different in the case that it updates the two involved clusters if a object is moved to another cluster if I'm right.</p>
  
  <p>Nevertheless, I still do not see in which points these Algorithms are different.</p>