3.1.3. Step three
The main aim of this step is for every class in __ds to have an average value representative. Technically speaking, every class has a number of observations, and each observation has \(n\) variables; on the basis of the assumption outlined in (\ref{932063}), the sum of the \(n\) variables of each observation is sought. This is formulated as follows:
\(\begin{equation} \label{eq:4}
\sum_{i=1}^{n} x_i
\end{equation}\)
As a result, each class will have several sums from Equation (\ref{eq:4}); an average of the sums is calculated as follows:
\(\begin{equation} \label{eq:5}
\frac{\min_{sum}+\max_{sum}}{2}
\end{equation} \)
Finally, the averages of each class collectively form a vector \(v\) of size \(k\), where \(k\) is equal to the number of classes in __ds. Vector \(v\) is named parameter_4 and is referred to as avg vector. Fig. (\ref{135163}) illustrates the outcome from this step using the showcase example in (\ref{993524}).