Cluster

Group your data points based on similarities between them

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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.

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.
Relevance AI's platform provides you with a no-code workflow to cluster your vectorized data with a few clicks. Makes sure to follow the vectorize guide if your dataset does not include vectors.

Once you have uploaded and vectorized your data, select your dataset and click on Cluster under Workflows and follow the guide. The image below shows how to cluster a dataset based on the description field using the Kmeans algorithm.

Relevance AI - cluster workflow setup for clustering data using the Kmeans algorithm.Relevance AI - cluster workflow setup for clustering data using the Kmeans algorithm.

Relevance AI - cluster workflow setup for clustering data using the Kmeans algorithm.

After running this workflow, clustering results are automatically added to your dataset under a new field (cluster.descriptionmpnet_vector.kmeans-10 in our example). Check the results under the Dataset -> Monitor -> Clusters.

Relevance AI - Monitor clustering results in the datasetRelevance AI - Monitor clustering results in the dataset

Relevance AI - Monitor clustering results in the dataset

Next step: use the Apps section to configure the best visualization for the found insights.


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