 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, 21.9.2000

