 Usage:  ck = kmcont (x, k, t)  
 
 Input:

  x                      n x p matrix of n row points to be clustered (the 
                         elements must be >= 0 with positive marginal 
                         sums) 
                         
  k                      scalar: The number of clusters 
                         
  t                      n x 1 matrix of the true partition (only if known, 
                         else a matrix containing 1) 
                         
                         
 Output:

  ck.y                   n x l matrix: correspondence analysis scores of the 
                         row points (l = min(n-1,p-1) 
                         
  ck.z                   p x l matrix: correspondence analysis scores of the 
                         column points 
                         
  ck.b                   n x 1 matrix: Partition of n points into k clusters 
                         
  ck.c                   k x p matrix of average profiles of clusters 
                         
  ck.v                   k x p matrix of within cluster inertias divided by 
                         the corresponding weights (masses) of clusters 
                         
  ck.s                   k x 1 matrix of weights (total row profile) of the 
                         rows 
                         
  ck.a                   p x 1 matrix of weights (inverse total column 
                         profile) of the columns 
                         
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(C) MD*TECH Method and Data Technologies, 17.8.2000
