Does this Claude Code skill handle data cleaning?

Yes, the skill includes automated preprocessing steps such as data scaling and normalization, which are essential for accurate clustering results.

Which clustering algorithms are supported by this skill?

The skill supports a variety of popular algorithms including K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and hierarchical clustering.

How do I evaluate the quality of the clusters produced?

The skill automatically calculates and reports performance metrics such as the silhouette score and the Davies-Bouldin index to help you determine the optimal number of clusters.

Can I see visualizations of my clusters?

Absolutely. The skill generates Python code using libraries like Matplotlib or Seaborn to create scatter plots and other visualizations of the resulting clusters.

Clustering Algorithms Suite

Name: Clustering Algorithms Suite
Author: jeremylongshore

byjeremylongshore

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883

数据科学与机器学习

Executes and analyzes machine learning clustering algorithms to identify patterns, groups, and anomalies within datasets.

关于

This skill empowers Claude to perform advanced data grouping using popular machine learning algorithms like K-means, DBSCAN, and hierarchical clustering by automating the entire pipeline from data preprocessing to visualization. It is particularly useful for data scientists and analysts who need to quickly uncover hidden structures, segment customers, or detect anomalies within complex datasets without manually writing boilerplate scikit-learn code. By handling data validation, performance metrics like silhouette scores, and artifact generation, it streamlines the exploratory data analysis process directly within the Claude Code environment.

主要功能

883 GitHub stars
Support for K-means, DBSCAN, and hierarchical clustering algorithms
Generation of performance metrics like silhouette scores and Davies-Bouldin index
Built-in anomaly detection capabilities for identifying data outliers
Integrated data visualization with cluster-labeled scatter plots
Automated data preprocessing including scaling and normalization

使用场景

Segmenting customer databases into distinct personas for targeted marketing
Discovering natural groupings in high-dimensional scientific or financial data
Detecting anomalous patterns in network traffic for security monitoring

关于

主要功能

883 GitHub stars
Support for K-means, DBSCAN, and hierarchical clustering algorithms
Generation of performance metrics like silhouette scores and Davies-Bouldin index
Built-in anomaly detection capabilities for identifying data outliers
Integrated data visualization with cluster-labeled scatter plots
Automated data preprocessing including scaling and normalization

使用场景

Segmenting customer databases into distinct personas for targeted marketing
Discovering natural groupings in high-dimensional scientific or financial data
Detecting anomalous patterns in network traffic for security monitoring