The Silhouette Analysis Tool is an advanced interactive utility designed to evaluate the quality of clustering results. It calculates and visualizes the silhouette score, which measures how similar a data point is to its own cluster compared to other clusters. This tool helps users determine the optimal number of clusters and assess the effectiveness of their clustering models.

Key Features:

1. Dynamic Input Parameters: Users can specify the number of data points, feature dimensions, and clustering range.
2. Silhouette Score Visualization: The tool generates a silhouette plot using Chart.js, providing a clear visual representation of cluster cohesion and separation.
3. PDF Export Functionality: A fully functional PDF download button allows users to save the silhouette plot for reporting or further analysis.
4. Modern and Attractive Design: The tool features a colorful, stylish interface that enhances user engagement while maintaining usability.
5. Self-Contained Container: The tool operates within its own container, ensuring it does not interfere with the page’s header or footer.

Use Cases:

• Data scientists validating the performance of clustering algorithms.
• Researchers exploring the structure of unlabeled datasets.
• Educators teaching clustering techniques in machine learning courses.
• Business analysts segmenting customers or products for targeted strategies.

How It Works:

1. Users input the number of data points, feature dimensions, and the range of clusters to analyze.
2. The tool generates random data and applies K-Means clustering for each cluster value in the specified range.
3. For each clustering result, the silhouette scores are calculated and displayed as a bar chart.
4. Users can interact with the plot and download it as a PDF for offline use.