Abstract
The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. (Source code available at: http://github.com/blueocean92/deep_constrained_clustering)
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Bade, K., Nürnberger, A.: Creating a cluster hierarchy under constraints of a partially known hierarchy. In: SIAM (2008)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: KDD (2004)
Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. CRC Press, Boca Raton (2008)
Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: ICML (2004)
Chatziafratis, V., Niazadeh, R., Charikar, M.: Hierarchical clustering with structural constraints. arXiv preprint arXiv:1805.09476 (2018)
Dao, T.B.H., Vrain, C., Duong, K.C., Davidson, I.: A framework for actionable clustering using constraint programming. In: ECAI (2016)
Davidson, I., Ravi, S.: Intractability and clustering with constraints. In: ICML (2007)
Davidson, I., Wagstaff, K.L., Basu, S.: Measuring constraint-set utility for partitional clustering algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 115–126. Springer, Heidelberg (2006). https://doi.org/10.1007/11871637_15
Fogel, S., Averbuch-Elor, H., Goldberger, J., Cohen-Or, D.: Clustering-driven deep embedding with pairwise constraints. arXiv preprint arXiv:1803.08457 (2018)
Gress, A., Davidson, I.: Probabilistic formulations of regression with mixed guidance. In: ICDM (2016)
Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: IJCAI (2017)
Hsu, Y.C., Kira, Z.: Neural network-based clustering using pairwise constraints. arXiv preprint arXiv:1511.06321 (2015)
Jiang, Z., Zheng, Y., Tan, H., Tang, B., Zhou, H.: Variational deep embedding: An unsupervised and generative approach to clustering. arXiv preprint arXiv:1611.05148 (2016)
Joachims, T.: Optimizing search engines using clickthrough data. In: KDD (2002)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. JMLR 5, 361–397 (2004)
Lu, Z., Carreira-Perpinan, M.A.: Constrained spectral clustering through affinity propagation. In: CVPR (2008)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR (2015)
Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: NIPS (2004)
Shaham, U., Stanton, K., Li, H., Nadler, B., Basri, R., Kluger, Y.: Spectralnet: spectral clustering using deep neural networks. arXiv preprint arXiv:1801.01587 (2018)
Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Workshop on Artificial Intelligence for Web Search (AAAI 2000), vol. 58, p. 64 (2000)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: AAAI (2000)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al.: Constrained k-means clustering with background knowledge. In: ICML (2001)
Wang, X., Davidson, I.: Flexible constrained spectral clustering. In: KDD (2010)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: ICML (2016)
Xing, E.P., Jordan, M.I., Russell, S.J., Ng, A.Y.: Distance metric learning with application to clustering with side-information. In: NIPS (2003)
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: SIGIR (2003)
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. arXiv preprint arXiv:1610.04794 (2016)
Acknowledgements
We acknowledge support for this work from a Google Gift entitled: “Combining Symbolic Reasoning and Deep Learning”.
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Zhang, H., Basu, S., Davidson, I. (2020). A Framework for Deep Constrained Clustering - Algorithms and Advances. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_4
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