1. Input Data Formats; 
 a. Each pattern must have inputs followed by 0 or more 
    outputs. Therefore, training data files will work.
 b. Training data for classification typically has N features 
    followed by the class id. 
 c. Training data for mapping typically has N
    features followed by several desired output values. 

2. Output Data Format;
   Output files from clustering include the number 
   of clusters, followed by the cluster vectors themselves.

3. Conventional Clustering; 
 a. Cluster a data file using Sequential Leader or 
    K-Means Clustering. 
 b. Desired outputs, if any, can be ignored.

4. Self-Organizing Map; 
 a. Cluster a data file using Kohonen's Self-Organizing 
    Feature Map.
 b. Desired outputs, if any, can be ignored.

5. Error Function

   The error function that is being minimized during K-Means 
   clustering and self-organizing map training is

                    N      
    MSE = (1/Npat) SUM MSE(k)     where
                   k=1  

              Npat                     2
    MSE(k) =  SUM [ x(p,k) - m(i(p),k ]   ,
              p=1  

    Npat is the number of training patterns, N is the number 
    of inputs per pattern, x(p,k) is the kth input sample from the
    pth pattern, m(i,k) is the kth sample from the ith cluster, and
    i(p) is the index of the cluster to which the pth pattern
    belongs
