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. Processing Example for Conventional Clustering
 a. Under the "Conventional 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 training set
0       ! read all patterns in the file
1       ! start clustering
15.     ! threshold for sequential leader clustering
2       ! refine the clusters using K-Means
10      ! number of K-Means iterations
3       ! stop clustering
1       ! save clusters
cl      ! filename for saved clusters

    we see that the program will apply sequential leader clustering
    to the file Twod.tra, with a threshold of 15. Then 10 iterations of
    K-Means clustering will be used. The clusters will be saved in 
    a file called cl.
 c. After running the program, we can "Examine Program Output",
    where we observe that the normalized clustering error is 3.200854.

