Enhanced Clustering with UMAP and HDBSCAN

📅 2025-05-17 — Session: Enhanced Clustering with UMAP and HDBSCAN

🕒 22:25–23:25
🏷️ Labels: HDBSCAN, UMAP, Clustering, Data Visualization, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance clustering techniques using UMAP for dimensionality reduction and HDBSCAN for clustering, with a focus on parameter tuning and visualization.

Key Activities

• Adjusted parameters for HDBSCAN to optimize cluster numbers, focusing on min_cluster_size, min_samples, and cluster_selection_epsilon.
• Explored latent structures using UMAP with projections in more than two dimensions to gain additional insights.
• Implemented Python code to visualize UMAP projections and evaluate clustering quality using silhouette scores.
• Resolved size conflicts in UMAP visualizations by adjusting slicing strategies.
• Developed a loop for iterating through UMAP projection axes to enhance data visualization.
• Created a customizable HDBSCAN clustering function with parameter exploration capabilities.
• Designed an exploratory loop to analyze clustering results, including noise clusters.
• Proposed a hyperparameter explorer for HDBSCAN to optimize cluster numbers and minimize noise.

Achievements

• Successfully optimized HDBSCAN parameters for better clustering results.
• Enhanced data visualization through improved UMAP projections.
• Developed tools for systematic exploration of clustering configurations.

Pending Tasks

• Further testing and validation of the hyperparameter explorer for HDBSCAN.
• Integration of clustering results into broader data analysis workflows.