Agent skill · data analytics · aj-geddes
Clustering Analysis
Identify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning
Why this skill is useful
Provides specific implementations and techniques for clustering analysis that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Clustering Analysis Overview Clustering partitions data into groups of similar observations without pre-defined labels, enabling discovery of natural patterns and structures in data. When to Use Segmenting customers based on purchasing behavior or demographics Discovering natural groupings in data without prior knowledge of categories Identifying market segments for targeted marketing campaigns Organizing large datasets into meaningful categories for further analysis Finding patterns in gene expression data or medical imaging Grouping documents, products, or users by similarity for recommendation systems Clustering Algorithms K-Means: Partitioning into k clusters Hierarchical: Dendrograms showing nested clusters DBSCAN: Density-based arbitrary-shaped clusters Gaussian Mixture: Probabilistic clustering Agglomerative: Bottom-up hierarchical approach Key Concepts Cluster Validation: Metrics to evaluate cluster quality Optimal Clusters: Methods to determine best k Inertia: Within-cluster sum of squares Silhouette Score: Measure of cluster separation Dendrogram: Hierarchical clustering visualization Implementation with Python Cluster Quality Metrics Silhouette Score: -1 to 1 (higher is better) Davies-Bouldin Index: Lower is better Calinski-Harabasz Index: Higher is better Inertia: Lower is better (KMeans only) Algorithm Selection K-Means: Fast, spherical clusters, k needs specification Hierarchical: Produces dendrogram, interpretable DBSCAN: Arbitrary shapes, handles noise GMM: Probabilistic, soft assignments Deliverables Optimal cluster count analysis Cluster visualizations Validation metrics comparison Cluster characteristics summary Silhouette plots Dendrogram for hierarchical clustering Membership assignments
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/clustering-analysis