Does it help in finding the optimal number of clusters?

Yes, it provides implementation patterns for the Elbow Method and Silhouette Analysis to mathematically determine the most appropriate number of clusters for your dataset.

Can this skill be used for data visualization?

Absolutely. It includes techniques for dimensionality reduction like PCA, t-SNE, and UMAP to project high-dimensional data into 2D or 3D space for easier visualization.

Which clustering algorithms does this skill support?

The skill supports a wide range of algorithms including K-Means for spherical clusters, DBSCAN and HDBSCAN for density-based clusters, and Agglomerative Hierarchical clustering.

Is anomaly detection included in this skill?

Yes, the skill provides specific patterns for detecting outliers using Isolation Forests and Local Outlier Factor (LOF) methods.

Do I need to scale my data before using these tools?

Yes, the skill emphasizes best practices like feature scaling (StandardScaler) as a required step to ensure clustering algorithms perform accurately.

Clustering & Pattern Discovery

Name: Clustering & Pattern Discovery
Author: pluginagentmarketplace

bypluginagentmarketplace

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Data Science & ML

Discover hidden structures and anomalies in unlabeled data using advanced unsupervised learning algorithms and dimensionality reduction.

About

The Clustering skill enables Claude to perform sophisticated unsupervised learning tasks, helping developers identify meaningful patterns within complex, unlabeled datasets. It provides a standardized framework for implementing popular algorithms like K-Means, DBSCAN, and HDBSCAN, alongside dimensionality reduction techniques like PCA and UMAP for data visualization. By incorporating automated validation metrics and anomaly detection patterns, this skill ensures that data discovery processes are statistically rigorous, scalable, and ready for production environments.

Key Features

Dimensionality reduction for visualization via PCA, t-SNE, and UMAP
Automated optimal K-value discovery using silhouette and inertia metrics
Comprehensive cluster validation with Silhouette, Calinski-Harabasz, and Davies-Bouldin scores
Support for multiple clustering algorithms (K-Means, DBSCAN, Hierarchical, HDBSCAN)
Built-in anomaly detection using Isolation Forests and Local Outlier Factor
1 GitHub stars

Use Cases

Detecting fraudulent transactions or system anomalies in real-time streams
Segmenting customer or user behavior data for targeted personalization
Simplifying high-dimensional datasets for exploratory data analysis and visualization

About

Key Features

Dimensionality reduction for visualization via PCA, t-SNE, and UMAP
Automated optimal K-value discovery using silhouette and inertia metrics
Comprehensive cluster validation with Silhouette, Calinski-Harabasz, and Davies-Bouldin scores
Support for multiple clustering algorithms (K-Means, DBSCAN, Hierarchical, HDBSCAN)
Built-in anomaly detection using Isolation Forests and Local Outlier Factor
1 GitHub stars

Use Cases

Detecting fraudulent transactions or system anomalies in real-time streams
Segmenting customer or user behavior data for targeted personalization
Simplifying high-dimensional datasets for exploratory data analysis and visualization