Purpose

This section documents the DBSCAN algorithm configuration and validates clustering quality for the marketing spend analysis. The parameters control how density-based clusters are identified, while quality metrics confirm whether the discovered clusters are meaningful and well-separated. Understanding these settings is essential for interpreting the 7 clusters and assessing confidence in the segmentation results.

Key Findings

eps (0.765): Neighborhood radius automatically selected via k-distance elbow method; defines the maximum distance between points in the same cluster
minPts (5): Minimum points required to form a core point; ensures clusters contain sufficient density
Clusters Found (7): Seven distinct marketing spend segments identified across the 200 observations
Noise Points (9, 4.5%): Low noise rate indicates most observations fit well into clusters; outliers represent unusual spending patterns
Silhouette Score (0.632): Moderate-to-good cluster cohesion and separation; indicates reasonably distinct marketing segments

Interpretation

The algorithm successfully identified seven natural groupings in marketing spend behavior with minimal noise contamination. The silhouette score of 0.632 suggests clusters are reasonably well-defined, though not perfectly separated—typical for real-world marketing data where spending patterns may overlap. The auto-selected eps value balances sensitivity to local density variations while maintaining stable cluster