Visual analytics can be seen as an integral approach combining visualization, data analysis, and human factors. In order to gain knowledge from data, the visual analytics procedure unites visual analysis methods and automatic processes through human interaction. In many application scenarios, visual or automatic analysis methods were applied after the integration of heterogeneous data sources. Therefore, before performing visual analysis we should clean, normalize, and integrate the heterogeneous data sources. After the data cleaning, the analyst may choose visual analysis methods, wherein visualization helps the analyst to relate with the automatic methods by modifying parameters or selecting other analysis algorithms. At the end, the model visualization can be used to study the findings of the generated models.