Keras implementation for our IJCAI-17 paper:

• Xifeng Guo, Long Gao, Xinwang Liu, Jianping Yin. Improved Deep Embedded Clustering with Local Structure Preservation. IJCAI 2017.

and re-implementation for paper:

• Junyuan Xie, Ross Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. ICML 2016.

This code requires pretrained autoencoder weights provided. Use IDEC-toy code for a quick start.

1. Install Keras v2.0, scikit-learn and git
   sudo pip install keras scikit-learn
sudo apt-get install git

2. Clone the code to local.
   git clone https://github.com/XifengGuo/IDEC.git IDEC

3. Prepare datasets.

    cd IDEC/data/usps   
    bash ./download_usps.sh   
    cd ../reuters  
    bash ./get_data.sh   
    cd ../..
   

4. Get pre-trained autoencoder's weights.
   Follow instructions at https://github.com/piiswrong/dec to pre-train the autoencoder. Then save the trained weights to a keras model (e.g. mnist_ae_weights.h5) and put it in folder 'ae_weights'.
   If you do not want to install Caffe package, you can download the pretrained weights from
   https://github.com/XifengGuo/data-and-models
   Then put .h5 file in ae_weights in local folder 'ae_weights'.
   Or you can just use IDEC-toy code for a quick start, but the results may be not promising.

5. Run experiment on MNIST.
   python IDEC.py mnist --ae_weights ae_weights/mnist_ae_weights.h5
   or
   python DEC.py mnist --ae_weights ae_weights/mnist_ae_weights.h5
   The IDEC (or DEC) model is saved to "results/idec/IDEC_model_final.h5" (or "results/dec/DEC_model_final.h5").

6. Run experiment on USPS.
   python IDEC.py usps --ae_weights ae_weights/usps_ae_weights --update_interval 30
python DEC.py usps --ae_weights ae_weights/usps_ae_weights --update_interval 30

7. Run experiment on REUTERSIDF10K.
   python IDEC.py reutersidf10k --ae_weights ae_weights/reutersidf10k_ae_weights --n_clusters 4 --update_interval 3
python DEC.py reutersidf10k --ae_weights ae_weights/reutersidf10k_ae_weights --n_clusters 4 --update_interval 20

The DEC model:

The IDEC model: