> For the complete documentation index, see [llms.txt](https://docs.gaiodataos.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gaiodataos.com/tools/tasks/analytics/cluster.md). # Cluster
The **Cluster** task in Gaio DataOS applies clustering algorithms to **group records with similar characteristics**. It's ideal for use cases such as customer segmentation, pattern recognition, and data-driven decision-making based on behavioral or structural profiles. Gaio uses the **K-Means** technique to identify groups and analysis calculations are made in H2O, whose documentation can be [accessed here](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/k-means.html). *** ## How to Use the Cluster Task *** ### 1. **Open the Cluster Task** * In the **Studio**, go to the **Tasks** panel. * Under the **Analytics** section, select **Cluster**. *** ### 2. **Configure the Task** * **Task label**: (optional) Name for identifying this step in your flow. * **Result table:** Output table that will contain the clustered results. Example: `cluster_campaign.` * **Table name**: Automatically populated with the selected table (e.g., `new_sales`). *** ### 3. **Exclude Columns (Optional)** * In the **Exclude columns** field, add columns that should **not be considered** in the clustering process, such as unique IDs (e.g., `cod_cliente`). * This helps avoid bias or noise in the algorithm. *** ### 4. **Adjust Execution Settings** **Execution time** * Defines the **maximum runtime** of the clustering algorithm (in seconds). * Recommended: between **20 and 60 seconds**, depending on dataset size and complexity. **Max cluster size** * Sets the **maximum number of clusters** the algorithm can create. * Example: if set to `3`, the output will contain up to 3 distinct groups. **️ Automatic clusters size** * When enabled, Gaio will **automatically determine the ideal number of clusters** based on the data's variability. * When disabled, it will strictly follow the manual limit set in **Max cluster size**. *** ### 5. **Save and Run** * Click **Save** to confirm the task configuration. * Run the flow — the output table will contain your clustered data. *** #### Output The resulting table will include: * All original columns (excluding those set to be ignored) * A new column indicating the **assigned cluster ID** for each row *** #### Best Practices * Use tasks like **Sample** or **Principal Component Analysis (PCA)** beforehand to reduce dimensionality and improve performance. * Remove irrelevant or high-cardinality columns that could distort clustering results. * Leverage clustering to personalize campaigns, identify customer profiles, detect anomalies, or support retention strategies.