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Novella
Solutions
70 lines
1.3 KiB
Markdown
70 lines
1.3 KiB
Markdown
# Clustering
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Group features based on spatial proximity using clustering algorithms.
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## Overview
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Clustering groups nearby features into clusters, identifying spatial patterns and groupings in point data.
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## Inputs
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- **Dataset**: Point dataset
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- **Method**: Clustering algorithm
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- **Parameters**: Algorithm-specific parameters
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## Outputs
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New dataset containing:
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- Original features
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- **Cluster ID**: Assigned cluster identifier
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- **Cluster Size**: Number of features in cluster
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- Original attributes
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## Algorithms
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### K-Means Clustering
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Groups points into k clusters by minimizing within-cluster variance.
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### DBSCAN
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Density-based clustering that identifies clusters of varying shapes.
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### Hierarchical Clustering
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Builds cluster hierarchy using distance measures.
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## Example
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```json
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{
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"dataset_id": 123,
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"method": "kmeans",
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"k": 5
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}
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```
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## Background Jobs
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This analysis runs as a background job.
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## Use Cases
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- Market segmentation
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- Service area identification
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- Pattern recognition
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- Data exploration
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## Notes
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- Algorithm selection depends on data characteristics
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- Parameter tuning affects results
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- Results may vary with different random seeds
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- Consider spatial scale when interpreting clusters
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## Related Documentation
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- [Analysis API](../api/analysis.md)
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