Abstract
Collaborative clustering aims at revealing the common structures of data distributed on different sites using local clustering methods such as Self-Organizing Maps (SOM). To face the ever growing quantity of data available, incremental clustering methods are needed. This paper presents an algorithm to perform incremental SOM-based collaborative clustering without topological modifications of the map. The experiments conducted on several datasets demonstrate the validity of the method and present the influence of the batch size on the learning.
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References
Ghassany, M., Grozavu, N., Bennani, Y.: Collaborative clustering using prototype-based techniques. Int. J. Comput. Intell. Appl. 11(03), 1250017 (2012)
Pedrycz, W., Rai, P.: Collaborative clustering with the use of fuzzy c-means and its quantification. Fuzzy Sets Syst. 159(18), 2399–2427 (2008)
Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 36(4), 795–805 (2006)
Cornuéjols, A., Wemmert, C., Gançarski, P., Bennani, Y.: Collaborative clustering: Why, when, what and how. Inf. Fusion 39, 81–95 (2018)
Grozavu, N., Cabanes, G., Bennani, Y.: Diversity analysis in collaborative clustering. In: 2014 International Joint Conference on Neural Networks, pp. 1754–1761. IEEE (2014)
Filali, A., Jlassi, C., Arous, N.: Som variants for topological horizontal collaboration. In: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 459–464. IEEE (2016)
Rastin, P., Cabanes, G., Grozavu, N., Bennani, Y.: Collaborative clustering: How to select the optimal collaborators? In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 787–794. IEEE (2015)
Bishop, C.M., Svensén, M., Williams, C.K.: GTM: The generative topographic mapping. Neural Comput. 10(1), 215–234 (1998)
Sublime, J., Grozavu, N., Cabanes, G., Bennani, Y., Cornuéjols, A.: From horizontal to vertical collaborative clustering using generative topographic maps. Int. J. Hybrid Intell. Syst. 12(4), 245–256 (2015)
Ghassany, M., Grozavu, N., Bennani, Y.: Collaborative multi-view clustering. In: The 2013 International Joint Conference on Neural Networks, pp. 1–8. IEEE (2013)
Wang, Y., Chen, L., Mei, J.P.: Incremental fuzzy clustering with multiple medoids for large data. IEEE Trans. Fuzzy Syst. 22(6), 1557–1568 (2014)
Deng, D., Kasabov, N.: ESOM: An algorithm to evolve self-organizing maps from online data streams. In: Neural Networks, vol. 6, pp. 3–8. IEEE (2000)
Papliński, A.P.: Incremental self-organizing map (iSOM) in categorization of visual objects. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7664, pp. 125–132. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34481-7_16
UCI: Machine Learning Repository. https://archive.ics.uci.edu/ml/index.php
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Maurel, D., Sublime, J., Lefebvre, S. (2017). Incremental Self-Organizing Maps for Collaborative Clustering. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_52
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DOI: https://doi.org/10.1007/978-3-319-70087-8_52
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