https://github.com/a11155/k-means-clustering
Midterm Project for MATH 478 Topological Data Analysis
Science Score: 13.0%
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Low similarity (6.1%) to scientific vocabulary
Last synced: 9 months ago
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Repository
Midterm Project for MATH 478 Topological Data Analysis
Basic Info
- Host: GitHub
- Owner: a11155
- Language: Python
- Default Branch: main
- Size: 8.79 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created over 1 year ago
· Last pushed over 1 year ago
Metadata Files
Readme
README.md
K-means-Clustering
Midterm Project for MATH 478: Topological Data Analysis
This project implements and visualizes the K-Means Clustering algorithm using Python.
Features
- Supports multiple distance metrics: Euclidean, Cosine, and Manhattan.
- Visualizes the clustering process and centroid movements.
- Offers various data generation options: Gaussian, Moon, Spiral, Swiss Roll, and more.
- Provides clustering performance metrics: Silhouette Score, Davies-Bouldin Index, and Inertia.
Installation and Setup
- Clone the repository
bash git clone https://github.com/a11155/K-means-Clustering.git cd K-means-Clustering - Install the required packages
bash pip install -r requirements.txt - Run the application using Streamlit
bash streamlit run main.py
Owner
- Name: Andrii Kryvenko
- Login: a11155
- Kind: user
- Repositories: 1
- Profile: https://github.com/a11155
GitHub Events
Total
- Push event: 3
- Create event: 1
Last Year
- Push event: 3
- Create event: 1
Dependencies
requirements.txt
pypi
- numpy *
- pandas *
- plotly *
- scikit-learn *
- streamlit *