GACluster -- Graph Agglomerative Clustering Library

Version 0.1.0

Table of Contents

• Introduction
• Citations
• Benchmark Results
• Quick start with MATLAB
• Changes
• References
• Clustering Project
• Homepage

Introduction

The GACluster open source library implements popular Graph Agglomerative Clustering algorithms.

GACluster is distributed under the BSD license (see the COPYING file).

Two major limits of previous GAC toolbox are 1) memory cost and 2) C++ MEX implementation. This new version only includes pure MATLAB code and is optimized for memory. E.g., [5] did not report the result of GDL [1] on 70000 samples of MNIST full dataset due to huge memory cost, and it is possible to run GDL with this library if you have 24G memory. Further optimization is possbile with less readability.

Citations

Please cite the following papers, if you find the code is helpful.

  @inproceedings{zhang2012graph,
    title={Graph degree linkage: Agglomerative clustering on a directed graph},
    author={Zhang, Wei and Wang, Xiaogang and Zhao, Deli and Tang, Xiaoou},
    booktitle={European Conference on Computer Vision},
    pages={428--441},
    year={2012}
  }

  @article{zhang2013agglomerative,
    title={Agglomerative clustering via maximum incremental path integral},
    author={Zhang, Wei and Zhao, Deli and Wang, Xiaogang},
    journal={Pattern Recognition},
    volume={46},
    number={11},
    pages={3056--3065},
    year={2013}
  }

Benchmark Results

GDL without deep representation achieves close performance to state-of-the-art deep clustering algorithms.

Clustering performances of different algorithms in terms of NMI/ACC.

DataSet	MNIST	MNIST-test	USPS	Fashion-MNIST
GDL [1]	0.910/0.964	0.864/0.933	0.860/0.922	0.660/0.627
[Deep Clustering]
DEC [9]	-/0.843	-/-	-/-	-/-
JULE [3]	0.913/0.964	0.915/0.961	0.913/-	-/-
DEPICT [4]	0.917/0.965	0.915/0.963	0.927/0.964	-/-
VaDE [10]	-/0.945	-/-	-/-	-/-
DAC [11]	0.935/0.978	-/-	-/-	-/-
DBC [12]	0.917/0.964	-/-	0.724/0.743	-/-
ConvDEC-DA [7]	0.960/0.985	0.958/0.983	0.962/0.987	0.636/0.586
DDC-DA [13]	0.941/0.969	0.927/ 0.970	0.939/0.977	0.661/0.609
ClusterGAN [14]	0.921/0.964	-/-	0.931/0.970	-/-

Quick start with MATLAB

GACluster provides a number of demos for reproducing results on popular datasets.

Changes

• Initial public release.

TODO

References

[1] W. Zhang, X. Wang, D. Zhao and X. Tang. Graph degree linkage: Agglomerative clustering on a directed graph. ECCV, 2012.

[2] W. Zhang, D. Zhao and X. Wang. Agglomerative clustering via maximum incremental path integral. Pattern Recognition, 46(11), pp.3056-3065, 2013.

[3] J. Yang, D. Parikh and D. Batra. Joint unsupervised learning of deep representations and image clusters. CVPR, 2016. Paper Code

[4] K.G. Dizaji, A. Herandi, C. Deng, W. Cai, H. Huang. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. ICCV, 2017. Paper Code

[5] S.A. Shah and V. Koltun. Robust continuous clustering. PNAS, 114(37), pp.9814-9819, 2017. Paper Code

[6] Shah, S.A. and Koltun, V., Deep Continuous Clustering. Paper Code

[7] X. Guo, E. Zhu, X. Liu and J. Yin. Deep embedded clustering with data augmentation. ACML, 2018. Paper Code

[8] X. Guo, L. Gao, X. Liu and J. Yin. Improved deep embedded clustering with local structure preservation. IJCAI, 2017. Paper Code

[9] J. Xie, R. Girshick and A. Farhadi. Unsupervised deep embedding for clustering analysis. ICML, 2016.

[10] Z. Jiang, Y. Zheng, H. Tan, B. Tang and H. Zhou. Variational deep embedding: An unsupervised and generative approach to clustering. IJCAI, 2017. Paper Code

[11] J. Chang, L. Wang, G. Meng, S. Xiang and C. Pan. Deep adaptive image clustering. ICCV, 2017. Paper Code

[12] F. Li, H. Qiao and B. Zhang. Discriminatively boosted image clustering with fully convolutional auto-encoders. Pattern Recognition, 83, 2017. Paper

[13] Y. Ren, N. Wang, M. Li and Z. Xu. Deep Density-based Image Clustering. arXiv preprint arXiv:1812.04287, 2018. Paper

[14] K.G. Dizaji, X. Wang, C. Deng and H. Huang. Balanced Self-Paced Learning for Generative Adversarial Clustering Network. CVPR, 2019. Paper

[15] W. Hu, T. Miyato, S. Tokui, E. Matsumoto and M. Sugiyama. Learning Discrete Representations via Information Maximizing Self-Augmented Training. ICML, 2017. Paper Code

[16] U. Shaham, K. Stanton, H. Li, B. Nadler, R. Basri and Y. Kluger. SpectralNet: Spectral Clustering Using Deep Neural Networks. ICLR, 2018. Paper Code

[17] X. Guo, X. Liu, E. Zhu, X. Zhu, M. Li,X. Xu and J. Yin. Adaptive Self-paced Deep Clustering with Data Augmentation. IEEE TKDE, 2019. Paper Code