

 Unsupervised Learning

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

 B. Output Data Format
    The K-Mean and SOM algorithm out file is a cluster center file
    which include triaining data file name, No of elements per pattern,
    No of elements used for clustering, No of clusters followed by each
    cluster center.
    The classify vectors algorithm output file is patterns followed by 
    the membership.

 C. Available Algorithms 
  1. Cluster a data file using K-Means Clustering whose initial
     cluster centers got by sequential leader algorithm. 
  2. Cluster a data file using Kohonen's Self-Organizing Feature Map
     with random initial cluster center.
  3. Classify a data file using clusters from K-Means or Self-Organizing 
     Map clustering. The number of the cluster to which each vector is
     closest is determined.

 D. Error Function for Unsupervised Learning

   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


