Introduction

Clustering is a fundamental task in data analysis, where the goal is to partition data into groups (clusters) such that data points within the same group are more similar to each other than to those in other groups. This project provides an extensive toolkit for performing clustering using unsupervised machine learning methods, enabling you to explore patterns, discover insights, and derive meaningful conclusions from your data.

Features

• Variety of Algorithms: Implementations of popular clustering algorithms such as K-means.
• Scalability: Designed to handle large datasets efficiently, ensuring scalability and performance.
• Evaluation Metrics: Comprehensive evaluation metrics to assess the quality of clustering results and aid in algorithm selection.
• Visualization: Tools for visualizing clustering results to gain intuitive insights into data patterns and structures.
• Flexibility: Easily adaptable to different types of data and clustering tasks, providing flexibility in usage.