This is the code corresponding to the experiments conducted for the work "Deep constrained clustering applied to satelliteimage time series" (Baptiste Lafabregue, Jonathan Weber, Pierre Gançarki & Germain Forestier) This work was presented at MACLEAN workshop at ECML/PKDD Conference 2019 (https://mdl4eo.irstea.fr/maclean-machine-learning-for-earth-observation/)

Experiments were done with Python 3.7 and the following packages:

• Numpy
• Matplotlib
• Keras
• Pandas
• Scikit-learn
• Scipy

This code should execute correctly with last versions of these packages.

The dataset used for the paper is not available but it can be tested on time-series datasets, such as as the UEA archive: http://www.timeseriesclassification.com/ univariate or multivariate. The script ts_to_a2cnes_format can be used to convert sk-time format files to our format.

To train a model on the Mallat dataset from the UCR archive you have to train first an autoencoder with mlp or fcnn architecture:

with fcnn: python FCNN_AE.py Univariate Mallat --itr "1" --epochs=700 --batch_size=8 with mlp: python MLP_SDAE.py Univariate Mallat --itr "1" --epochs=200 --epochs_final=400 --batch_size=8

Then, you can train the constrained clustering as follow: python MLP_DEC.py fcnn Mallat 5 0.1 --archive_name Univariate --itr "0" --ae_weights "ae_weights/fcnn/Mallat1/Mallat-pretrain-model-700_z10.h5" --batch_size=8