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UMAP

ML Models & Algorithms
Used in BERTopic for efficient dimensionality reduction of embeddings.

UMAP is an algorithm used for Dimensionality Reduction of embeddings. Its primary function within the BERTopic architecture is to reduce the high dimensionality of BERT embeddings (e.g., converting 384-dimensional vectors down to 2 or 3 dimensions).
This reduction enables more efficient visualization and clustering. UMAP is essential for BERTopic because the subsequent clustering algorithm, HDBSCAN, operates on this low-dimensional space to identify dense clusters.

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

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