# 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).
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## How to Use the Cluster Task
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### 1. **Open the Cluster Task**
* In the **Studio**, go to the **Tasks** panel.
* Under the **Analytics** section, select **Cluster**.
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### 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`).
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### 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.
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### 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**.
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### 5. **Save and Run**
* Click **Save** to confirm the task configuration.
* Run the flow — the output table will contain your clustered data.
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#### 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
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#### 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.
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