Analyzing and interpreting high-dimensional data is a significant challenge for researchers and data scientists owing to data complexity. t-SNE and UMAP have long been the favorites as dimensionality reduction techniques that have provided tools to visualize the data in lower-dimensions. Hence, the complicated datasets become a more manageable and understandable form. These methods either by focusing on preserving the local structure of the data or by prioritizing the most significant features, enable data visualization which helps a more comprehensible way to analyze the data. Meanwhile, interpreting these visualizations provided by aforementioned methods remains a remarkable challenge. Therefore, a need arises for more interpretable and understandable data visualizations. This need is not for convenience purposes alone, but to have more interpretable visualizations in data-driven research.