The operation classifies data records by a TS-SOM. It creates a classified data in which classes correspond to neurons. Each class has indexes which refer the data records belonging to this class. Note that it is possible to use other weights (or fields) to classify data as were used in the training (see Sect. 5.1.1). To do that, you should select these weights to some data frame, and then give this frame to the command somcl with the flag -w.
Also, missing values may be noticed. Then, only such fields that do not include missing values are used to compute the distance between a data record and the neurons of the SOM. See also the operation msdbycen from Sect. 4.1.4 to replace missing values by the values of the prototypes.
Example (ex5.2): First a TS-SOM is trained by a data and then the same data is classified by the same TS-SOM. The second example below uses the result of this example, but loaded from a file.
... NDA> prepro -d boston -dout predata -e -n NDA> somtr -d predata -sout som1 -l 4 NDA> somcl -d predata -s som1 -cout cld1 # store trained TS-SOM NDA> save som1 -o som1 ...
Example: This example shows the case in which a TS-SOM has been trained earlier and it has been stored in a file. The TS-SOM and a data are loaded from the files, and then the SOM classification is performed. The loaded data is preprocessed first, because the TS-SOM was trained by preprocessed data (see the previous example).
NDA> load som1.som # the name of the TS-SOM is tssom NDA> load boston.dat # the name of the data is boston NDA> prepro -d boston -dout preboston NDA> somcl -d preboston -s som1 -cout cld2 ...