README.txt

This is an introduction of the code developed for the Deep Clustering Network (DCN). Please direct your emails to

Bo Yang, yang4173@umn.edu

if you have troubles running the code, or find any bugs.

Here is the paper: arxiv: https://arxiv.org/pdf/1610.04794v1.pdf Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos and Mingyi Hong "Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering"

============================================== Main files

run_rcv1.py : Script to reproduce our results on RCV1 dataset (Table 1). run_20News.py : Script to reproduce our results on 20Newsgroup dataset (Table 2). run_raw_mnist.py : Script to reproduce our results on raw-MNIST dataset (Table 3). run_pre_mnist.py : Script to reproduce our results on pre-processed MNIST dataset (Table 4). run_pendigits.py : Script to reproduce our results on Pendigits dataset (Table 5). multi_layer_km.py : Main file for defining the network, as well as various utility functions.

-- More documentations can be found inside each of the above files.

There are some additional python source files in the repository, which were developed for trying out various ideas. They are kept for possible future use, but are less documented (unfortunately...).

============================================== Data preparation

The data file should be named like 'something.pkl.gz', i.e., it should be pickled and compressed by gzip, using python code as follow:

""" with gzip.open('something.pkl.gz', 'wb') as f: cPickle.dump([train_x, train_y], f, protocol = 0) """ where train_x and train_y are numpy ndarray with shape train_x: (n_samples, n_features) train_y: (n_samples, )

The path of data files should be made available to the programs by modifying the 'path' local variable in each of the 'run_' python source files.

============================================== Dependencies

Theano
scikit-learn numpy scipy matplotlib Theanon deep learning tutorial code (can be download from http://deeplearning.net/tutorial/).