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clustering

Cluster 2D numeric arrays using kmeans, dbscan, hierarchical, and hdbscan algorithms.

1|1|Updated Nov 18, 2025
Search Tags:#data-analysis#cluster#anomaly-detection#dimensionality-reduction#unsupervised-learning#kmeans#dbscan
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill clustering-pluginagentmarketplace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clustering
Source: https://github.com/pluginagentmarketplace/custom-plugin-machine-learning/tree/main/skills/clustering
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill clustering-pluginagentmarketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Cluster unlabeled data to reveal underlying structure and groupings.

Core Features & Use Cases

  • Unsupervised pattern discovery in large datasets to segment data without labels
  • Dimensionality reduction and visualization for exploratory analysis
  • Anomaly detection and outlier identification to improve data quality
  • Use Case: preprocess customer data to identify segments for targeted campaigns

Quick Start

Provide a numeric dataset X as a 2D array to run k-means clustering and obtain cluster labels.

Frequently Asked Questions about clustering

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

FAQPage Schema
How do I cluster unlabeled data to find customer segments?▼

To cluster unlabeled data for customer segmentation, provide a numeric dataset as a 2D array to run algorithms like k-means and obtain cluster labels. This process reveals underlying groupings for targeted campaigns without needing predefined categories.

What unsupervised learning algorithms are available for anomaly detection?▼

Available unsupervised learning algorithms for anomaly detection include kmeans, dbscan, hierarchical, and hdbscan. These algorithms identify outliers and reveal underlying data structures to improve overall data quality during exploratory analysis.

Can I use dimensionality reduction for exploratory data analysis on 2D numeric arrays?▼

Yes, you can use dimensionality reduction and visualization for exploratory analysis on 2D numeric arrays. The clustering process validates input for 2D numeric arrays to reveal patterns and preprocess data for downstream ML pipelines.

How do I configure kmeans clustering parameters for my dataset?▼

You configure kmeans clustering parameters through the frontmatter-defined parameters exposed by the system. This configuration allows you to adjust algorithm settings to properly process your numeric dataset and output accurate cluster labels.

When should I use dbscan instead of hierarchical clustering for pattern discovery?▼

You choose dbscan or hierarchical clustering based on your data structure and desired grouping density. Dbscan excels at identifying outliers and arbitrarily shaped clusters, while hierarchical clustering builds a tree of groupings for exploratory pattern discovery.

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