<![CDATA[Correlation analysis and visual analysis of multidimensional datasets with the objective of identification of patterns and trends is an essential element of decision-making processes. Conventional visualization models in the considered area, such as correlation heatmaps, are used to visually represent the value of the correlation coefficient measured between pairs of attributes of the multidimensional dataset but are hard to read when working with a large number of attributes. This study concerns the design and implementation of a visualization model, which can be used to identify prediction potential hotspots in analysed datasets - parts of the dataset, which are strongly correlated with a high number of attributes in the dataset. The proposed model focuses on a graphical representation of such hotspots based on planar, multicomponent graphs, with the aim of meta-analysis of large, multidimensional datasets. The implemented approach is evaluated on a case study focused on the analysis of the original cubic graph property dataset where several prediction potential hotspots of different correlation types are constructed. Other than the construction of the hotspots themselves, this study shows a comparison of results gained by the graphical model to the conventional model used in the meta-analysis of multidimensional datasets – Shapley value explanations. The results presented in this study point to the need for a robust visualization framework for the analysis of correlation structures in multidimensional datasets and for models of visualization based on virtual and augmented reality.]]>
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