## 3 Problem Description and Proposed Method
The main-diagonal subplots are the univariate histograms (distributions) for each attribute. For example, the plot in the fourth row and the fourth column shows the univariate histogram for average-jitter, where the minimum and maximum of the parameter can be observed. On the non-diagonal subplots, bi-variate distribution between attributes can be observed. Some plots, such as those in the first column  
Figure 5: Snapshot of a D-ITG output. and the first row, represent separate clusters, which show that no relationship exists between the attributes. Certain scatter plots that follow a clear linear pattern with an increasing or decreasing slope can be seen, which shows that some conclusions can be drawn from the distribution. Plots with a linear relationship between the two attributes make it possible to identify in which space the classes will be well separated from each other. For example, the plot in the second row and the fourth column shows a linear relationship between the two variables. Such linear patterns represent dependencies among the attributes, bringing us to the conclusion that average delay and average jitter are correlated. Figure 6: Distribution range of sample dataset (507 samples), (**a**) Data samples for average delay, (**b**) Data samples for average jitter, (**c**) Data samples for average bit rate, (**d**) Data samples for average packet rate. The pair-plot is an efficient tool for exploratory data visualization and analysis but does not show the strength of correlation between the attributes with dependencies. This can be achieved through a correlation heat-map using the heatmap() function available in the Seaborn package. The correlation of all the numerical attributes as a heat-map using the Spearman correlation coefficient is shown in Figure 1:0 The scale of color in the heat-map varies from blue to yellow, where blue signifies a correlation of +1 and yellow signifies a correlation of \(-1\). The Spearman correlation coefficient measures the extent to which two variables tend to change together. The coefficient describes both the strength and the direction of the relationship. The Spearman's rank-order correlation is the non-parametric version of the Pearson correlation. [PERSON]'s correlation coefficient (\(r_{s}\)) measures the strength and direction of association or the monotonic relationship between two ranked variables. It requires two variables that are either ordinal, interval, or ratio. For a sample size of n,  
Figure 1:1 Pair-plot of dataset. the attributes \(X_{i}\) and \(Y_{i}\) are converted to ranks \(rg_{x}\) and \(rg_{y}\). Spearman correlation coefficient (\(r_{s}\)) is computed by Equation (1),  
Figure 1:2  
where \(cov(rg_{x},rg_{y})\) is the co-variance of the rank variables. \(\sigma_{r}g_{x}\) and \(\sigma_{r}g_{y}\) are standard deviations of the rank variables. It can be observed from Figure 8 that average delay and average jitter are highly correlated with a Spearman coefficient of 0.92. Similarly, bytes received, average bit rate, and average packet rate are highly correlated as well. Parameters, for example, average delay and average packet rate, can be observed to be negatively correlated, with a Spearman coefficient of \(-0.75\).