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When should you use dimensionality reduction before clustering?

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When should you use dimensionality reduction before clustering?

Asked on Oct 11, 2025

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Dimensionality reduction is often used before clustering to enhance performance and interpretability by reducing noise and computational complexity. Techniques like PCA or t-SNE can help in projecting high-dimensional data into lower-dimensional spaces, making clustering algorithms more efficient and effective.

Example Concept: Dimensionality reduction techniques, such as Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), are used to reduce the number of features in a dataset while preserving its variance or structure. This can improve clustering results by minimizing noise, reducing overfitting, and decreasing computational load, especially in high-dimensional datasets where clustering algorithms may struggle with the "curse of dimensionality."

Additional Comment:

Dimensionality reduction is beneficial when dealing with datasets with hundreds or thousands of features.
It helps in visualizing clusters in 2D or 3D space, making interpretation easier.
Be cautious of information loss; ensure that the reduced dimensions still capture the essential patterns of the data.
Consider the trade-off between dimensionality reduction and the interpretability of the clustering results.

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When should you use dimensionality reduction before clustering?

Asked on Oct 11, 2025

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