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KMEAN.HLP
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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,
16 ! number of inputs in a pattern
1 ! number of outputs in a pattern (class id not used)
Grng ! filename for shape recognition 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 Grng, 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 3.659437.