What is the relationship between dimensionality reduction and clustering stability?

Dimensionality reduction can either improve or destabilize clustering depending on information retention.

Reducing dimensions can remove noise and improve cluster separability, making algorithms like k-means more stable. However, excessive reduction may merge distinct clusters or distort density distributions, leading to unstable clustering results. The balance depends on intrinsic dimensionality of data.