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 sparser, making it difficult to find meaningful patterns or similarities. This happens because the volume of space expands exponentially, causing data to spread thinly across the higher-dimensional space. Consequently, algorithms struggle with tasks like clustering or classification, as the data doesn't provide enough density for reliable insights. In essence, higher dimensions make it harder to interpret data accurately, often requiring more sophisticated methods or larger datasets.