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

4. Processing Example for Self-Organizing Map
 a. Under the "Neural Clustering" option, choose "Batch Processing"
 b. From the parameter file,

8       ! number of inputs in a pattern
7       ! number of outputs in a pattern (class id not used)
Twod.tra ! filename for shape recognition training set
0       ! read all patterns in the file
1       ! initialize clusters
36      ! pick 36 as the number of clusters
20      ! number of iterations
2       ! use linearly decreasing learning factor and neighborhoods
.8  5   ! initial learning factor and half-neighborhood size
1       ! continue clustering
5       ! number of iterations
2       ! use linearly decreasing learning factor and neighborhoods
.04  0  ! initial learning factor and half-neighborhood size
2       ! stop
1       ! save clusters
sm      ! filename for saved clusters

    we see that the program will apply Self-Organizing Map clustering
    to the file Twod.tra with 20 iterations. The number of random initial 
    clusters is 36. The initial learning factor and half-neighborhood
    size are respectively .8 and 5, and linearly decreasing neighborhoods 
    and learning factor are chosen. After 20 iterations, 5 additional 
    iterations are specified. The clusters will be saved in a file 
    called sm.
 c. After running the program, we can "Examine Program Output",
    where we observe that the normalized clustering error is 2.84466.
