What are the challenges of clustering in high-dimensional spaces?

What are the challenges of clustering in high-dimensional spaces?

Clustering in high dimensions can lead to issues like the curse of dimensionality, where distances become less meaningful.

Can you explain the curse of dimensionality?

Sure! The curse of dimensionality refers to various phenomena that arise when analyzing data in high-dimensional spaces, making it difficult to find patterns.

What techniques can help with clustering in high dimensions?

Techniques like dimensionality reduction (PCA, t-SNE) and using distance metrics that are more suitable for high dimensions can help.

Are there specific algorithms that work better in high dimensions?

Yes, algorithms like DBSCAN and hierarchical clustering can be more effective in high-dimensional spaces compared to traditional methods.