# Clustering

Group features based on spatial proximity using clustering algorithms.

## Overview

Clustering groups nearby features into clusters, identifying spatial patterns and groupings in point data.

## Inputs

- **Dataset**: Point dataset
- **Method**: Clustering algorithm
- **Parameters**: Algorithm-specific parameters

## Outputs

New dataset containing:

- Original features
- **Cluster ID**: Assigned cluster identifier
- **Cluster Size**: Number of features in cluster
- Original attributes

## Algorithms

### K-Means Clustering

Groups points into k clusters by minimizing within-cluster variance.

### DBSCAN

Density-based clustering that identifies clusters of varying shapes.

### Hierarchical Clustering

Builds cluster hierarchy using distance measures.

## Example

```json
{
  "dataset_id": 123,
  "method": "kmeans",
  "k": 5
}
```

## Background Jobs

This analysis runs as a background job.

## Use Cases

- Market segmentation
- Service area identification
- Pattern recognition
- Data exploration

## Notes

- Algorithm selection depends on data characteristics
- Parameter tuning affects results
- Results may vary with different random seeds
- Consider spatial scale when interpreting clusters

## Related Documentation

- [Analysis API](../api/analysis.md)