This repository contains the code used in our paper: E2DTC: An End to End Deep Trajectory ClusteringFramework via Self-Training

• Ubuntu OS
• Python >= 3.5 (Anaconda3 is recommended)
• PyTorch 1.4+
• A Nvidia GPU with cuda 10.2+

Please refer to the source code to install all required packages in Python.

• Our geolife trajectory clustering datasets are stored in data according to our Ground Truth Generation algorithm.
• We provide cluster center data, raw trajectory data, as well as discretized token(cell size 300) for training.

1. Training with parameters

python e2dtc.py -vocab_size 1319 -knearestvocabs "data/geolife-vocab-dist-cell300.h5" -pretrain_epoch 20 -num_layers 3 -learning_rate 0.0001 -gamma 0.1 -beta 0.1 -update_interval 1 -n_cluster 12 -cuda 0

2. The training produces two model checkpoint.pt and best_model.pt, checkpoint.pt contains the latest trained model and best_model.pt saves the model which has the best performance on the validation data.

Some code comes from t2vec.