Operations for computing statistics in classes follow the same principle. An operation takes a classified data as a parameter. Then it collects all the data records pointed by these classes and computes statistical values for them. The result will be a data frame in which each class will have a data record and one statistical value over all the classes is stored in a field. The principle of these operations have been described in the following figure:
This macro command computes statistics for classes in the classified data <cldata> from the data <srcdata>. The procedures called by this command are documented in their own sections below.
Example (ex4.9): In the first example, the Boston data is classified between unique values in the field "chas" which has two values: the apartment is on river side or not. Then the average values are computed from other fields. Also the numbers of the items in both classes are found.
... NDA> select key1 -f boston.chas NDA> uniq -d key1 -cout cld1 NDA> clstat -d boston -c cld1 -dout sta -avg -hits NDA> ls -fr sta -f hits crim_avg zn_avg ...
Example (ex4.10): The second example demonstrates computing statistics for neurons. One advantage of the statistical values comparing with weights is that they have clear interpretations such as the mean, the minimum and maximum values of the data records in neurons.
... NDA> somtr -d predata -sout som1 -l 4 NDA> somcl -d predata -s som1 -cout cld1 NDA> clstat -d boston -c cld1 -dout sta -all ...