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}
}