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Train Clustering (DBSCAN) Node

AI/ML/Clustering

Train Clustering (DBSCAN)

Fit/Train DBSCAN Density-Based Clustering

fit_dbscanml
Inputs4
Outputs3
Security exposure4/10
Packageml

Ratings

Scores range from 0 to 10. Higher values mean more impact, exposure, or operational weight.

SecurityAttack surface and exposure impact.
4/10Medium
PrivacyPotential sensitivity of processed data.
4/10Medium
PerformanceRuntime or resource pressure.
5/10Medium
GovernancePolicy, audit, or compliance impact.
4/10Medium
ReliabilityOperational stability considerations.
3/10High
CostExternal or compute cost impact.
3/10High

Input Pins

4

Input

Execution
exec_in

Execution trigger that begins clustering

Epsilon

Float
epsilon

Maximum distance between points in the same cluster

Default 0.5
Range 0.01 to 10

Min Points

Integer
min_points

Minimum points required to form a dense region

Default 5
Range 1 to 100

Data Source

String
source

Choose which backend supplies the training data

Default Database
Database

Output Pins

3

Done

Execution
exec_out

Activated once clustering completes

Clusters

Integer
n_clusters

Number of clusters found (excluding noise)

Noise Points

Integer
n_noise

Number of points classified as noise

Node Info

Internal name
fit_dbscan
Category
AI/ML/Clustering