Abstract
In the collaborative clustering framework, the hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution. The goal is that each local computation, quite possibly applied to distinct data sets, benefits from the work done by the other collaborators. This article is dedicated to collaborative clustering based on the Learning Using Privileged Information paradigm. Local algorithms weight incoming information at the level of each observation, depending on the confidence level of the classification of that observation. A comparison between our algorithm and state of the art implementations shows improvement of the collaboration process using the proposed approach.
The authors thank Facebook France for financially supporting this research.
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References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22(5–6), 544–557 (2009)
Vapnik, V., Izmailov, R.: Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16, 2023–2049 (2015)
Pedrycz, W.: Collaborative fuzzy clustering. Pattern Recogn. Lett. 23(14), 1675–1686 (2002)
Cornuéjols, A., Wemmert, C., Gançarski, P., Bennani, Y.: Collaborative clustering: Why, when, what and how. Inf. Fusion 39, 81–95 (2018)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, Kluwer Academic Publishers, New York (1981). https://doi.org/10.1007/978-1-4757-0450-1
Wemmert, C., Gançarski, P., Korczak, J.J.: A collaborative approach to combine multiple learning methods. Int. J. Artif. Intell. Tools 09(01), 59–78 (2000)
Ghassany, M., Grozavu, N., Bennani, Y.: Collaborative generative topographic mapping. In: Huang, T., Zeng, Z., Li, C., Leung, C. (eds.) Neural Information Processing - 19th International Conference, ICONIP 2012, Doha, Qatar, November 12–15 (2012) Proceedings, Part II. Lecture Notes in Computer Science, vol. 7664, pp. 591–598. Springer, Heidelberg (2012)
Sublime, J., Matei, B., Cabanes, G., Grozavu, N., Bennani, Y., Cornuéjols, A.: Entropy based probabilistic collaborative clustering. Pattern Recogn. 72, 144–157 (2017)
Ghassany, M., Grozavu, N., Bennani, Y.: Collaborative multi-view clustering. In: The 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, USA, August 4–9, 2013, pp. 1–8. IEEE (2013)
Grozavu, N., Bennani, Y.: Topological collaborative clustering. Aust. J. Intell. Inf. Process. Syst. 12(3), 1–6 (2010)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Wang, G.-G., Deb, S., Cui, Z.: Monarch butterfly optimization. Neural Comput. Appl. 31(7), 1995–2014 (2019)
Foucade, Y.: CoLUPI: learning from data and learners [Source code and more material] https://github.com/yfoucade/colupi
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Foucade, Y., Bennani, Y. (2021). Unsupervised Learning from Data and Learners. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_48
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