https://github.com/fonzy0508/clustering-distribution-visualization-learning
Visualizing clustering distributions on the Digits dataset using UMAP, PCA, t-SNE, and algorithms like K-Means, DBSCAN, and Hierarchical Clustering.
https://github.com/fonzy0508/clustering-distribution-visualization-learning
clustering clustering-algorithm data-visualization dbscan dimensionality-reduction kmeans machine-learning pca python tsne umap
Last synced: 4 months ago
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Visualizing clustering distributions on the Digits dataset using UMAP, PCA, t-SNE, and algorithms like K-Means, DBSCAN, and Hierarchical Clustering.
- Host: GitHub
- URL: https://github.com/fonzy0508/clustering-distribution-visualization-learning
- Owner: Fonzy0508
- Created: 2025-02-24T02:15:09.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-24T06:12:54.000Z (9 months ago)
- Last Synced: 2025-02-24T06:22:02.614Z (9 months ago)
- Topics: clustering, clustering-algorithm, data-visualization, dbscan, dimensionality-reduction, kmeans, machine-learning, pca, python, tsne, umap
- Language: Python
- Homepage:
- Size: 3.91 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Learning & Clustering Explorations
Welcome! 😊 This repository focuses on visualizing and exploring different deep learning and clustering techniques. The study involves using **Autoencoders**, **dimensionality reduction techniques**, and **clustering algorithms** to analyze high-dimensional data.
## 📊 Visualizations
This study emphasizes visual analysis, including:
- **Reconstructed images** (if applicable)
- **2D & 3D embeddings** after dimension reduction
- **Clustering results** across different techniques
## 🛠 Tools & Libraries
This project was built using **Google Colab** with:
- **TensorFlow Keras** - for deep learning models 🤖
- **Matplotlib** - for visualization 📊
- **NumPy** - for numerical computations 🔢
- **Scikit-learn** - for clustering and reduction algorithms 🏗️
- **UMAP** - for nonlinear dimensionality reduction 🚀
## 🎯 Goals
- Explore how different **Autoencoders** transform data.
- Compare **dimensionality reduction** techniques.
- Evaluate how **clustering algorithms** perform on reduced data.
- Provide clear **visual insights** into high-dimensional datasets.