https://github.com/lirongwu/dcv
Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
https://github.com/lirongwu/dcv
clustering geometric-deep-learning high-dimensional-data manifold-learning visualization
Last synced: 6 months ago
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Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
- Host: GitHub
- URL: https://github.com/lirongwu/dcv
- Owner: LirongWu
- License: mit
- Created: 2022-02-14T12:25:25.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-16T04:23:41.000Z (almost 3 years ago)
- Last Synced: 2025-03-27T07:48:11.056Z (7 months ago)
- Topics: clustering, geometric-deep-learning, high-dimensional-data, manifold-learning, visualization
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 9
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Clustering and Visualization (DCV)
This is a PyTorch implementation of the DCV, and the code includes the following modules:
* Datasets (MNIST, HAR, USPS, Pendigits, Reuters-10K, Coil100)
* Training for DCV-encoder and DCV-decoder
* Visualization
* Evaluation metrics
## Requirements
* pytorch == 1.6.0
* scipy == 1.3.1
* numpy == 1.18.5
* scikit-learn == 0.21.3
* umap == 1.18.5
* networkx == 2.3
## Description
* main.py
* Train() -- Train a new model
* Test() -- Test the learned model for evaluating generalization
* dataloader.py
* GetData() -- Load data of selected dataset
* model.py
* LISV2_MLP() -- model and loss
* tool.py
* GIFPloter() -- Auxiliary tool for online plot
* DataSaver() -- Save intermediate and final results
* cluster_acc() -- Calculate clustering accuracy
## Dataset
The datasets and pretrained models used in this paper are available in:
https://drive.google.com/file/d/19oO9l9WgnPZuqojKFVtwIRFm4s0vcY02/view?usp=sharing
## Running the code
1. Install the required dependency packages
2. To get the results on a specific *dataset*, run with proper hyperparameters
```
python main.py --data_name dataset
```
3. To get the data, metrics, and visualisation, refer to
```
../log/dataset/
```
where the *dataset* is one of the six datasets (MNIST, HAR, USPS, Pendigits, Reuters-10K, Coil100)
## Citation
If you find this project useful for your research, please use the following BibTeX entry.
```
@article{wu2022deep,
title={Deep Clustering and Visualization for End-to-End High-Dimensional Data Analysis},
author={Wu, Lirong and Yuan, Lifan and Zhao, Guojiang and Lin, Haitao and Li, Stan Z},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022},
publisher={IEEE}
}
```