mars-clustering

name	mars-clustering
description	How to optimize DBSCAN hyperparameters for Mars cloud clustering. Use this skill whenever performing grid search, DBSCAN clustering, or evaluating F1 and delta for Mars datasets.

Mars Cloud Clustering Optimization Workflow

1. Data Preparation

• Load citsci_train.csv and expert_train.csv from /root/data/.
• Group by file_rad to match annotations.

2. DBSCAN Custom Distance Metric

Implement the custom distance function: d(a, b) = sqrt((w * Δx)² + ((2 - w) * Δy)²)

3. Evaluation Metrics

• F1 Score: Harmonic mean of Precision and Recall.
• Delta: Standard Euclidean distance between matched centroids and expert points.
• Greedy Matching: Match cluster centroids to expert points with max distance 100px.

4. Grid Search Parameters

• min_samples: [3, 4, 5, 6, 7, 8, 9]
• epsilon: [4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
• shape_weight: [0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]

5. Output

• Filter by mean F1 > 0.5.
• Identify Pareto-optimal frontier.
• Save to /root/pareto_frontier.csv.