Abstract
While in standard clustering no side information is used, users might be interested in providing additional information to influence the clustering. In case of document clustering, additional information can take the form of pairwise constraints where a user provides additional information about pairs of documents as must-link and cannot-link constraints (indicating respectively whether the documents in the pair are coming from the same cluster or not). In this paper, we propose a novel deep document clustering framework which can employ pairwise constraints while learning document representations to obtain better tailored results. Indeed, in our proposed framework, data representations (obtained through an autoencoder) and cluster representatives are learned through back propagation in a joint way. Devising a fully differentiable deep clustering framework with the ability of using pairwise constraints is the main contribution of this paper. Experiments conducted on 5 public datasets show the gain in clustering performance which the resulting approach can yield.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, C., Chen, S., Xing, J., Sun, A., Ma, Z.: Seed-guided topic model for document filtering and classification. ACM Trans. Inf. Syst. 37(1), 9 (2018)
Chen, X., Xia, Y., Jin, P., Carroll, J.: Dataless text classification with descriptive LDA. In Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Li, C., Xing, J., Sun, A., Ma, Z.: Effective document labeling with very few seed words: a topic model approach. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 85–94 (2016)
Shental, N., Bar-hillel, A., Hertz, T., Weinshall, D.: Computing Gaussian mixture models with EM using equivalence constraints. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, pp. 465–472. MIT Press (2004)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. Int. Conf. Mach. Learn. 1, 577–584 (2001)
Liu, Y., Jin, R., Jain, A.K.: BoostCluster: boosting clustering by pairwise constraints. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 450–459. New York, NY, USA (2007)
Shah, S.A., Koltun, V.: Deep continuous clustering (2018). arXiv preprint arXiv:1803.01449
Huang, P., Huang, Y., Wang, W., Wang, L.: Deep embedding network for clustering. In: 2014 22nd International Conference on Pattern Recognition, pp. 1532–1537 (2014)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487 (2016)
Hu, Y., Wang, J., Yu, N., Hua, X.-S.: Maximum margin clustering with pairwise constraints. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 253–262 (2008)
Zeng, H., Cheung, Y.: Semi-supervised maximum margin clustering with pairwise constraints. IEEE Trans. Knowl. Data Eng. 24(5), 926–939 (2012)
Basu, S., Banerjee, A., Mooney, R.J.: Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 2004 SIAM International Conference on Data Mining, 0 vols., Society for Industrial and Applied Mathematics, pp. 333–344 (2004)
Xu, L., Neufeld, J., Larson, B., Schuurmans, D.: Maximum margin clustering. In: Advances in Neural Information Processing Systems, pp. 1537–1544 (2005)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), pp. 18–28 (1998)
Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: IJCAL, pp. 1753–1759 (2017)
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3861–3870 (2017)
Fard, M.M., Thonet, T., Gaussier, E.: Deep k-means: jointly clustering with k-means and learning representations (2018). arXiv preprint arXiv:1806.10069
Chen, G.: Deep learning with nonparametric clustering (2015). arXiv preprint arXiv:1501.03084
Guo, X., Zhu, E., Liu, X., Yin, J.: Deep embedded clustering with data augmentation. In: Asian Conference on Machine Learning, pp. 550–565 (2018)
Banijamali, E., Ghodsi, A.: Fast spectral clustering using autoencoders and landmarks. In: International Conference Image Analysis and Recognition, pp. 380–388 (2017)
Affeldt, S., Labiod, L., Nadif, M.: Spectral Clustering Via Ensemble Deep Autoencoder Learning (SC-EDAE) (2019). arXiv preprint arXiv:1901.02291
Ghasedi Dizaji, K., Herandi, A., Deng, C., Cai, W., Huang, H.: Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5736–5745 (2017)
Hsu, C.-C., Lin, C.-W.: CNN-based joint clustering and representation learning with feature drift compensation for large-scale image data. IEEE Trans. Multimed. 20(2), 421–429 (2017)
Yang, J., Parikh, D., Batra, D.: Joint unsupervised learning of deep representations and image clusters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5147–5156 (2016)
Chang, J., Wang, L., Meng, G., Xiang, S., Pan, C.: Deep adaptive image clustering. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5880–5888, Venice (2017)
Li, X., Li, C., Chi, J., Ouyang, J., Li, C.: Dataless text classification: a topic modeling approach with document manifold. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 973–982, New York, NY, USA (2018)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Acknowledgement
This research was partly funded by the ANR project LOCUST and the AURA project AISUA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fard, M.M., Thonet, T., Gaussier, E. (2020). Pairwise-Constrained Deep Document Clustering. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2019. Lecture Notes in Networks and Systems, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-44610-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-44610-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44609-3
Online ISBN: 978-3-030-44610-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)