title: Avoiding local minima when using Kmeans
body: <p>I'm using the following code for clustering with KMeans from sklearn.cluster.KMeans</p>
  
  <pre><code>from sklearn.cluster import KMeans
  
  num_clusters = 60
  
  km = KMeans(n_clusters=num_clusters, init="k-means++", n_init = 100)
  
  %time km.fit(newvec)
  
  clusters = km.labels_.tolist()
  </code></pre>
  
  <p>To avoid local minima I use n_init = 100. What else can be done to avoid falling into local minima. 
  I appreciate your help.</p>