DBSCAN Specialist — Scaffold Workflow

Description: Generate new artifact from scratch

When to Use

Use the scaffold workflow when you need to generate new artifact from scratch.

Input Requirements

• Datasets with distance metric specifications and dimensionality profiles
• Domain knowledge about expected cluster shapes, densities, and noise levels
• Scalability requirements and computational budget constraints
• Cluster validation criteria and quality metric targets

Process

1. Initialize — Set up the scaffold context for DBSCAN Specialist
2. Execute — Perform the scaffold operation following DBSCAN Specialist's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• Trained clustering models with parameter configuration documentation
• Parameter selection reports with k-distance plots and sensitivity analysis
• Cluster quality metrics (silhouette, DBCV, noise ratio) with interpretation
• Scalability assessment reports with runtime and memory profiling

Quality Gates

• Epsilon and minPts choices must be justified with k-distance analysis or domain knowledge
• Cluster quality must be evaluated using multiple internal validation metrics
• Noise point handling must be documented with downstream impact analysis
• Scalability must be assessed for production data volumes