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

{
  "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