title: No convergence for hard competitive learning clustering (flexclust package)
body: <p>I am applying the functions from the flexclust package for hard competitive learning clustering, and I am having trouble with the convergence.</p>
  
  <p>I am using this algorithm because I was looking for a method to perform a weighed clustering, giving different weights to groups of variables. I chose hard competitive learning based on a response for a previous question (<a href="https://stackoverflow.com/questions/48901178/weighted-kmeans-r/48901416#48901416">Weighted Kmeans R</a>).</p>
  
  <p>I am trying to find the optimal number of clusters, and to do so I am using the function stepFlexclust with the following code:</p>
  
  <pre><code>new("flexclustControl") ## check the default values
  
  fc_control &lt;- new("flexclustControl")
  fc_control@iter.max &lt;- 500 ### 500 iterations
  fc_control@verbose &lt;- 1 # this will set the verbose to TRUE
  fc_control@tolerance &lt;- 0.01
  
  ### I want to give more weight to the first 24 variables of the dataframe
  my_weights &lt;-  rep(c(1, 0.064), c(24, 31)) 
  
  set.seed(1908)
  hardcl &lt;- stepFlexclust(x=df, k=c(7:20), nrep=100, verbose=TRUE, 
                FUN = cclust, dist = "euclidean", method = "hardcl", weights=my_weights, #Parameters for hard competitive learning
                control = fc_control,
                multicore=TRUE)
  </code></pre>
  
  <p>However, the algorithm does not converge, even with 500 iterations. I would appreciate any suggestion. Should I increase the number of iterations? Is this an indicator that something else is not going well, or did I a mistake with the R commands?</p>
  
  <p>Thanks in advance.</p>