Which clustering algorithms are supported by this skill?

The skill supports a variety of popular unsupervised learning algorithms including K-means, DBSCAN, and hierarchical clustering, typically implemented via the scikit-learn library.

Does this skill handle data preprocessing?

Yes, it automatically includes essential preprocessing steps such as feature scaling and normalization to ensure that the clustering results are accurate and unbiased.

Can I see a visual representation of the clusters?

Absolutely. The skill generates visual artifacts like scatter plots with cluster labels, making it easy to interpret the groupings and data structure.

Is this skill suitable for anomaly detection?

Yes, specifically when using the DBSCAN algorithm, the skill is highly effective at identifying outliers that do not belong to any dense cluster, which is ideal for anomaly detection.

Clustering Analysis Runner

Name: Clustering Analysis Runner
Author: intent-solutions-io

byintent-solutions-io

데이터 과학 및 ML

Executes and visualizes diverse clustering algorithms to identify patterns and groupings within complex datasets automatically.

소개

This skill empowers Claude to automate the end-to-end data science lifecycle for unsupervised learning tasks, ranging from data preprocessing and scaling to the execution of sophisticated algorithms like K-means, DBSCAN, and hierarchical clustering. It is specifically designed to help developers and data scientists quickly uncover hidden structures in data, segment audiences, or detect anomalies without the need to write repetitive boilerplate machine learning code. By providing built-in performance metrics and visualization capabilities, it transforms raw data into actionable insights directly within the Claude Code environment.

주요 기능

Automated data preprocessing including scaling and normalization
Support for K-means, DBSCAN, and hierarchical clustering algorithms
Generation of performance metrics like Silhouette and Davies-Bouldin indices
Integrated visualization of clusters and outliers via scatter plots
Robust error handling and data validation for ML pipelines
0 GitHub stars

사용 사례

Analyzing high-dimensional research data to discover natural groupings and structures
Identifying anomalous patterns in network traffic or financial transactions for security
Segmenting customer databases into distinct behavioral groups for targeted marketing

소개

주요 기능

Automated data preprocessing including scaling and normalization
Support for K-means, DBSCAN, and hierarchical clustering algorithms
Generation of performance metrics like Silhouette and Davies-Bouldin indices
Integrated visualization of clusters and outliers via scatter plots
Robust error handling and data validation for ML pipelines
0 GitHub stars

사용 사례

Analyzing high-dimensional research data to discover natural groupings and structures
Identifying anomalous patterns in network traffic or financial transactions for security
Segmenting customer databases into distinct behavioral groups for targeted marketing