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1994-08-19
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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.