Why do some clustering algorithms struggle with high-dimensional data?

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Why do some clustering algorithms struggle with high-dimensional data?

Asked on Oct 21, 2025

Answer

Clustering algorithms often struggle with high-dimensional data due to the "curse of dimensionality," which makes distance measures less meaningful and increases computational complexity. This can lead to poor cluster formation and reduced algorithm performance, as traditional clustering methods like k-means rely on distance metrics that become less effective in high-dimensional spaces.

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.

Additional Comment:

Consider using dimensionality reduction before clustering to improve performance.
Evaluate clustering results with silhouette scores or other validation metrics to ensure meaningful clusters.
Explore clustering algorithms designed for high-dimensional data, such as DBSCAN or spectral clustering.

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