https://github.com/labrijisaad/git-clustering

Enhanced and Repackaged GIT Clustering: This repository offers an open-source, enhanced version of the GIT (Graph of Intensity Topology) clustering algorithm.
https://github.com/labrijisaad/git-clustering

benchmarking-clustring-algo cicd clustring distributing git git-clustering packaging testpypi unsupervised-learning

Last synced: 1 day ago
JSON representation

Enhanced and Repackaged GIT Clustering: This repository offers an open-source, enhanced version of the GIT (Graph of Intensity Topology) clustering algorithm.

• Host: GitHub
• URL: https://github.com/labrijisaad/git-clustering
• Owner: labrijisaad
• Created: 2024-03-03T10:04:11.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-04-06T11:34:39.000Z (7 months ago)
• Last Synced: 2024-04-06T12:21:15.433Z (7 months ago)
• Topics: benchmarking-clustring-algo, cicd, clustring, distributing, git, git-clustering, packaging, testpypi, unsupervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 89.3 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Enhanced and Repackaged GIT Clustering 🌐🔍

📦 **Discover the Package on TestPyPI**: [git_cluster Package](https://test.pypi.org/project/git_cluster/)

🔍 **Dive Deeper in Our GitHub Repository**: [Git-Clustering GitHub Repo](https://github.com/labrijisaad/Git-Clustering)

## About 📖

This repository introduces an enhanced version of the GIT (Graph of Intensity Topology) clustering algorithm. It's been **augmented with additional methods**, **repackaged** for ease of use, and includes **comprehensive benchmarks** to demonstrate its performance. 🚀

## Features ✨

- **Broad Applicability:** Tested across a variety of datasets. 🌍 (See the benchmarks in the [notebooks/](https://github.com/labrijisaad/Git-Clustering/tree/main/notebooks)).

- **User-friendly Packaging:** Simplified integration into your projects. 📦

## Usage 🛠️

To get started, explore the [notebooks/Quick_Start_with_GIT.ipynb](https://github.com/labrijisaad/Git-Clustering/blob/main/notebooks/Quick_Start_with_GIT.ipynb) notebook for a step-by-step guide on applying this algorithm to your data.

## Testing in Google Colab 🧪

To validate the installation and functionality of the GIT Clustering package, you can either run the steps manually following the instructions below or click the **Open in Colab** button to open a Colab notebook where everything is set up for you.

[![Run in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labrijisaad/Git-Clustering/blob/main/notebooks/Quick_Start_with_GIT.ipynb)

### Manual Installation and Execution

Follow these steps to manually install the GIT Clustering package and test its functionality:

1. **Install the GIT Clustering package from TestPyPI and upgrade gdown for dataset downloading:**

    ```bash

    !pip install -i "https://test.pypi.org/simple/" git_cluster

    !pip install -U gdown

    ```

2. **Download the datasets and prepare it for use:**

    ```bash

    !gdown 1yNwCStP3Sdf2lfvNe9h0WIZw2OQ3O2UP && unzip datasets.zip

    ```

3. **Execute a sample clustering process:**

    ```python

    from git_cluster import GIT

    from utils import alignPredictedWithTrueLabels, autoPlot

    from dataloaders import Toy_DataLoader as Toy_DataLoader

    # Load the Circles Dataset

    X_circles, Y_circles_true = Toy_DataLoader(name='circles', 

                                              path="/content/datasets/toy_datasets").load()

    # Create an instance of the GIT clustering

    git = GIT(k=12, target_ratio=[1, 1])

    # Fit the GIT model to the dataset and predict cluster labels.

    Y_circles_pred = git.fit_predict(X_circles)

    # Plot the dataset and highlight the clusters with different colors.

    autoPlot(X_circles, Y_circles_pred)

    ```

## Acknowledgments 🎉

- We extend our thanks to the original authors of the GIT algorithm for their foundational work in `Clustering Based on Graph of Intensity Topology`:

  - Gao, Zhangyang and Lin, Haitao and Tan, Cheng and Wu, Lirong and Li, Stan and others.

## Citing This Work 📝

If you use the GIT Clustering algorithm in your research or project, please consider citing the original work:

```bibtex

@article{gao2021git,

  title={Git: Clustering Based on Graph of Intensity Topology},

  author={Gao, Zhangyang and Lin, Haitao and Tan, Cheng and Wu, Lirong and Li, Stan and others},

  journal={arXiv preprint arXiv:2110.01274},

  year={2021}

}

```

## Connect with me 🌐