Does it provide visual feedback?

Yes, the skill generates visualizations like scatter plots with cluster labels to help users intuitively understand the resulting data groupings.

Which clustering algorithms are supported by this skill?

The skill supports popular algorithms including K-means for spherical clusters, DBSCAN for density-based grouping and noise detection, and hierarchical clustering for tree-based structures.

How does it measure the quality of the clusters?

It calculates key performance metrics such as the Silhouette Score and Davies-Bouldin Index to provide a quantitative measure of how well the data has been grouped.

Can this skill handle raw, unscaled data?

Yes, the skill includes best practices for data preprocessing, such as scaling and normalization, to ensure that clustering results are accurate and not biased by different feature ranges.

Clustering Algorithm Runner

Name: Clustering Algorithm Runner
Author: BbgnsurfTech

byBbgnsurfTech

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Ciencia de Datos y ML

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

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The Clustering Algorithm Runner skill empowers Claude to perform sophisticated data analysis by implementing clustering techniques such as K-means, DBSCAN, and hierarchical clustering. It automates the end-to-end workflow—including data preprocessing, feature scaling, algorithm execution, and performance evaluation—to help users discover natural structures in their data. Whether you are performing customer segmentation or anomaly detection, this skill provides actionable insights through statistical metrics and visual representations.

Características Principales

Support for K-means, DBSCAN, and hierarchical clustering algorithms
Automated data preprocessing including scaling and normalization
Performance evaluation using Silhouette and Davies-Bouldin metrics
Visual generation of scatter plots and cluster assignments
Robust error handling and dataset validation
1 GitHub stars

Casos de Uso

Identifying distinct customer segments for targeted marketing campaigns
Detecting outliers and anomalies in network traffic or financial logs
Exploratory data analysis to discover hidden structures in unlabeled datasets

Acerca de

Características Principales

Support for K-means, DBSCAN, and hierarchical clustering algorithms
Automated data preprocessing including scaling and normalization
Performance evaluation using Silhouette and Davies-Bouldin metrics
Visual generation of scatter plots and cluster assignments
Robust error handling and dataset validation
1 GitHub stars

Casos de Uso

Identifying distinct customer segments for targeted marketing campaigns
Detecting outliers and anomalies in network traffic or financial logs
Exploratory data analysis to discover hidden structures in unlabeled datasets