## 6 Results and Discussion
The MLMV-GKU shows the density clusters of each variable by means of automatically-generated contour lines, which can be easily identified by the naked eye. Furthermore, Figure 6 shows a considerable amount of clusters between different variables. If there are clusters due to two or more variables, the color in the shared area is mixed and shows the clusters with a different color. With the basic knowledge of color formation theory, it is possible to identify those areas easily with the naked eye. Additionally, with image processing, it is also possible to identify these areas by analyzing the color values of pixels. When \"Layer 1\" is considered, there are no contour lines due to less overlapping. However, in Layer 1, clusters due to different variables can be easily identified due to the different color of the shared regions (e.g., the shared area of TVFA and AA). Furthermore, it is possible to distinguish clusters belonging to different variables. Since the place value of Layer 1 is higher than the place value of Layer 0, the presence of color in a higher layer implies the existence of higher density in the position in relation to the respective pixel. Therefore, the examination of higher layers makes higher density area identification much simpler. Changing parameter values opens new ways of identifying new relations between different variables [27]. This technique is used as a very efficient tool in most data mining techniques. The MLMV-GKU also supports this feature and easily seeks relations by changing the overlapping between different variables. Figure 6 shows layers of the MLMV-GKU of the same dataset (scaled with the relative scaling technique), which is used in Figure 7, and scaled using \"absolute scaling\". The highest range among all of the dependent variables belongs to TVFA. Since \(R_{maxD}=8\), according to Equation (12), \(F_{iD}=50\) for TVFA, AA and PA. Then, according to Equation (13), \(new\_max_{A}\) for TVFA, AA and PA is 400, 250 and 250, respectively. Both layers in Figures 6 and 7 show overlapping areas of different variables. However, due to different scaling methods, the overlapping areas belong to different variables in Figures 6 and 7 which are not identical. This is a very useful feature when the GKU is used as a knowledge unit. When there is overlapping between variables, it is easy to identify color values of multiple parameters by analyzing a single pixel. This will reduce the overhead of computing. However, to interpret the meaning of a shared area due to one or more variables, sealing factors must be decided after properly investigating the domain requirements. Otherwise, only creating overlap areas between variables may not provide useful information. Selecting different color schemes for representing the shape color of DPSs has considerable impact on the visual representation ability of GKU. In general, there are two available color schemes for representing three dependent variables using the 24-bit RGB pixel format as (255, 255, CI), (255, CI, 255), (CI, 255, 255) and (0, 0, CI), (0, CI, 0), (CI, 0, 0). Each color in the first version is near to the white color, which is represented as (255, 255, 255), and each color in the second version is near to the black color, which is represented as (0, 0, 0). Figure 8 elaborates the impact of using the color scheme for the color of DPSs. Figure 8 contains the same data used in Figure 6 with the same scaling parameters. However, (0, 0, 20), (0, 20, 0) and (20, 0, 0) were used as colors of DPSs instead of (255, 255, 20), (255, 20, 255) and (20, 255, 255). The colors (255, 255, 20), (255, 20, 255) and (20, 255, 255) are not identical and are visually identifiable. Though colors (0, 0, 20), (0, 20, 0) and (20, 0, 0) are numerically identical, they are not visually identifiable because all of these colors are equivalent to the black color. The major drawback of this scheme is that the shape colors of DPSs cannot be visually identifiable, though they are different colors.