How To Subcluster Data In Relevance AI

Subclustering is the process of breaking existing group/clusters of data into smaller ones based on their similarities, trends and existing patterns.

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Why subclustering can be beneficial?

Subclustering helps analyse data in a deeper and more fine-grained way. An example would be having a main cluster on all insurance claims and breaking it down into claims involving cars, houses, fire, flood, ect. Such a break-down can make data analysis much easier and more insightful.

Relevance AI's platform provides you with a no-code workflow to subcluster your clustered data with a few clicks. Makes sure to follow the cluster workflow guide if your dataset isn't clustered.

Once you have uploaded, vectorized and clustered your data, select your dataset and run the Subcluster workflow. The images below show how to subcluster a dataset based on the already existing 10 clusters using the Kmeans algorithm. This setup will break each cluster into two clusters.

Each section in the setup is activated by clicking on the small blue dot on the left-hand side or by following the process which starts with "Get started", filling the data and clicking on "Continue".

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Relevance AI - Subclustering workflow

more details are available at subcluster workflow.


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