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ds-unsupervised-learning

Cluster unlabeled data with K-Means, hierarchical, and DBSCAN methods.

Updated Mar 8, 2026
Search Tags:#visualization#clustering#anomaly-detection#dimensionality-reduction#unsupervised-learning#recommender-systems
One-click install
npx skills add https://github.com/Phife726/ds_agent --skill ds-unsupervised-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ds-unsupervised-learning
Source: https://github.com/Phife726/ds_agent/tree/main/ds-unsupervised-learning
Command: npx skills add https://github.com/Phife726/ds_agent --skill ds-unsupervised-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Discover structure in unlabeled data by identifying clusters, reducing dimensionality for visualization, and generating simple recommendations to illuminate hidden patterns.

Core Features & Use Cases

  • Clustering: K-Means, hierarchical, and density-based methods to segment datasets and reveal natural groupings.
  • Dimensionality Reduction: PCA, t-SNE, and UMAP to compress features and visualize high-dimensional data.
  • Recommendation Foundations: Build basic item-to-user or item-to-item recommendations from unlabeled data; prototype hybrid strategies with minimal supervision.

Quick Start

Cluster this dataset using K-Means and visualize the first two principal components.

Frequently Asked Questions about ds-unsupervised-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform customer segmentation using unlabeled data?▼

Customer segmentation on unlabeled data is achieved by applying K-Means, hierarchical, or density-based clustering algorithms to reveal natural groupings within the dataset. You can evaluate clustering quality using silhouettes and elbow plots.

What is the best way to visualize high-dimensional data for exploratory analysis?▼

Visualizing high-dimensional data is best handled through dimensionality reduction techniques like PCA, t-SNE, and UMAP, which compress features to illuminate hidden patterns and simplify exploratory data analysis.

How do I build a recommender system from unlabeled data?▼

Building a recommender system from unlabeled data involves generating basic item-to-user or item-to-item recommendations to prototype hybrid strategies with minimal supervision.

When do I need to use DBSCAN versus K-Means for clustering?▼

You need DBSCAN for density-based clustering to find arbitrarily shaped groupings, whereas K-Means is suited for spherical clusters. Both clustering methods help reveal natural structures in datasets.

Can I detect anomalies using dimensionality reduction and clustering?▼

Anomaly detection is supported by leveraging clustering and dimensionality reduction to identify data points that deviate significantly from discovered natural groupings and compressed feature spaces.

How do I evaluate clustering quality without labeled training data?▼

Evaluating clustering quality without labels relies on internal validation metrics like silhouette scores and visual profiling tools such as elbow plots to assess the structure of natural groupings.

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