title: K-means with weighed variables
body: <p>I want to perform the K-means analysis where some of my variables should be considered more important than the others. I have found the kmeansw function, but after reading its help I am a bit confused:</p>
  
  <blockquote>
    <p>Usage</p>
    
    <p>kmeansW(x, centers, weight = rep(1,nrow(x)),
            iter.max = 10, nstart = 1) Arguments</p>
    
    <p>x </p>
    
    <p>A numeric vector, matrix or data frame.</p>
    
    <p>centers   </p>
    
    <p>Either the number of clusters or a set of initial (distinct) cluster
    centres. If a number, a random set of (distinct) rows in x is chosen
    as the initial centres.</p>
    
    <p>weight    </p>
    
    <p>weight of the elements of x. by default the same.</p>
    
    <p>iter.max  </p>
    
    <p>The maximum number of iterations allowed.</p>
    
    <p>nstart    </p>
    
    <p>If centers is a number, how many random sets should be chosen?</p>
  </blockquote>
  
  <p>Do I understand it right that it weighs elements of data frame (rows) and not variables (columns)? If this is right, what other way would you recommend for this problem?</p>