The Curse of Dimensionality

The Curse of Dimensionality refers to the challenges that arise when analyzing data with many features or variables. As the number of dimensions increases, data points become increasingly spread out and sparse, making it difficult to find meaningful patterns or similarities. This sparsity reduces the effectiveness of methods like clustering or distance measurements because data points no longer occupy a tightly packed space. Consequently, more data is needed to achieve reliable analysis, and many algorithms struggle to perform well in high-dimensional spaces. This phenomenon highlights the complexity and computational difficulty of working with large numbers of features.