Clustering groups items so that those in the same group/cluster have meaningful similarities (i.e. specific features or properties). Clustering facilitates informed decision-making by giving significant meaning to data through the identification of different patterns.
Why clustering data can be beneficial?
Clustering groups items so that those in the same group/cluster have meaningful similarities. Thus, clustering is a great tool to unravel hidden patterns in the data.
Cluster vs Auto cluster?
Both these workflows group data based on the existing conceptual similarities. Auto cluster decides on the number of clusters where cluster receives it as an input parameter.
We recommend trying both workflows and various number of clusters.
Relevance AI's platform provides you with a no-code workflow to cluster your vectorized data with a few clicks. Make sure to follow the vectorize workflow guide if your dataset does not include vectors.
The image below shows how to auto-cluster a dataset based on the vectorized description field.
After running this workflow, auto-clustering results are automatically added to your dataset under the new field.
Updated about 1 month ago