> For the complete documentation index, see [llms.txt](https://delphai.gitbook.io/delphai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://delphai.gitbook.io/delphai/en/documentation/images-and-media/clustering.md).

# Clustering

Clustering automatically identifies groups or patterns in a dataset, organizing similar elements together.

For example, when studying customers of a store, the method can reveal distinct groups, such as young people interested in technology or families purchasing household items, without these categories being predefined.

Clustering methods include:

* [**DBSCAN**](/delphai/en/documentation/images-and-media/clustering/dbscan.md): Identifies clusters based on the density of nearby points, ignoring noise.
* [**K-Means**](/delphai/en/documentation/images-and-media/clustering/k-means.md): Groups the data into "k" clusters by minimizing the variance within each group.
* [**Mean Shift**](/delphai/en/documentation/images-and-media/clustering/mean-shift.md): Groups by iteratively moving points to regions of higher density.