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title: Group Records Dynamically with Unsupervised Clustering
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/release-notes/264-0-0/rn_cdp_2026_summer_predictive_model_unsupervised_clustering.md
canonical_url: https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_predictive_model_unsupervised_clustering.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 74e78603e278b71d6c31f56a509b00948be65179b51308ae8417314db5711eec
description: To identify natural groupings and anomalies in your data quickly, cluster records dynamically without training or maintaining a reusable model. Unsupervised clustering uses pre-trained algorithms to group similar records. Use the capability to explore data, combine information from multiple columns into a single variable, or summarize millions of rows into a few meaningful clusters. For example, group related product or category records to preprocess data, and use the resulting cluster labels as variables in downstream forecasting or multi-class models.
products:
  - Cross Cloud
release: "264"
---
# Group Records Dynamically with Unsupervised Clustering

To identify natural groupings and anomalies in your data quickly, cluster records dynamically without training or maintaining a reusable model. Unsupervised clustering uses pre-trained algorithms to group similar records. Use the capability to explore data, combine information from multiple columns into a single variable, or summarize millions of rows into a few meaningful clusters. For example, group related product or category records to preprocess data, and use the resulting cluster labels as variables in downstream forecasting or multi-class models.

**Where:** This change applies to Lightning Experience in Developer, Enterprise, Performance, and Unlimited editions.

**When:** This functionality is available starting in August 2026.

**How:** In Data Cloud, go to the AI Models tab and select **Cluster**. Select a clustering model that best fits your data and requirements.

- K-Means: Divide records into a specified number of distinct clusters.
- HDBSCAN: Discover natural groupings in data with irregular patterns and identify outliers without specifying the number of clusters in advance.

Run a predict job to generate dynamic cluster assignments, such as cluster IDs and labels. Because clustering doesn’t require a separate training step, you can generate results without first creating a reusable model.

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-release-notes-salesforce_release_notes-264-0-0-production-enus/550ba85b-db1a-4608-9cd4-9a65ec96e1fa/images/icon_note.png)

Note For reusable cluster assignments and high-volume scoring across standard objects, use structured clustering, which uses a train-and-infer workflow.