Example Concept: In high-dimensional spaces, data points tend to become equidistant from each other, making it difficult for algorithms like k-means to identify distinct clusters based on distance. This phenomenon is known as the "curse of dimensionality." Additionally, the increased number of dimensions can lead to overfitting and require more computational resources, further complicating the clustering process. Dimensionality reduction techniques, such as PCA or t-SNE, are often employed to mitigate these issues by reducing the number of dimensions while preserving the data's structure.