Description: Dimensionality in data visualization refers to the number of attributes or characteristics that make up a dataset. Each dimension can be seen as a variable that provides information about the phenomenon being analyzed. As the number of dimensions increases, the complexity of visualization also rises, which can make data interpretation more challenging. In a two-dimensional space, for example, two variables can be easily represented in a scatter plot. However, when adding more dimensions, such as in a three-dimensional dataset, a more sophisticated approach is required to visualize and understand the relationships between variables. Dimensionality is also related to the concept of ‘the curse of dimensionality,’ which describes how increasing dimensions can make data sparse and difficult to analyze. Therefore, dimensionality reduction has become an important technique in data visualization, allowing analysts to simplify complex datasets and extract meaningful patterns without losing critical information. In summary, dimensionality is a fundamental aspect of data visualization that influences how data is presented and interpreted, directly affecting the clarity and effectiveness of visual communication.
