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Density-based Clustering

Task Types
Groups data points based on density and proximity. Does not require pre-defining the number of clusters and is good for finding arbitrarily shaped clusters and outliers.

Density-based clustering algorithms group data points based on their density and proximity. The approach identifies dense regions (topics) in the data space. Examples of algorithms in this category include DBSCAN and HDBSCAN.
This method is suitable for clustering both text and numeric data. In practice, BERTopic utilizes this clustering type, specifically employing HDBSCAN to identify dense clusters within the low-dimensional embedding space. Density-based clustering is used in SEO for tasks like backlink profile analysis and local search clustering.

Related Concepts

Distribution-based Clustering Centroid-based Clustering K-Means Clustering Hard Clustering Hierarchical Clustering

Sources & References

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

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