In this section we introduce operations needed for a TS-SOM. First, we give an overview how different data structures are used with a TS-SOM. The first operation is the training. It builds and organizes the TS-SOM structure which is stored in the name space with a given name. Suppose that its name is "som". In addition, the training creates a weight matrix, which is stored in a data frame called the name of the TS-SOM with the extension "_W", for instance, "som_W". In the data frame, each record includes the weights of one neuron, and it is connected to the corresponding neuron through its index. The TS-SOM structure maintains all the structural information about the neural network (see the figure below).
The second important operation is a SOM classification. The operation divides the data records of the given data frame into neurons. The result is a classified data which includes as many classes as there are neurons in the TS-SOM. Like a weight matrix, one class corresponds to one neuron and includes the indexes of the data records classified to the neuron (see the figure). If the neuron does not get any data record in the classification, then an empty class is created to correspond to this neuron.
