Abstract
This work introduces a novel approach to ensemble clustering by incorporating transfer learning. We address a clustering problem where, in addition to the data being analyzed, we have access to “similar” labeled data. The datasets may have different feature descriptions. Our method revolves around constructing meta-features that capture the structural characteristics of the data and transferring them from the source domain to the target domain. To define meta-features, we use the cluster ensemble method. Through extensive Monte Carlo modeling experiments, we have demonstrated the effectiveness of our proposed method. Notably, compared to other similar approaches, our method exhibits the capability to handle arbitrary feature descriptions in both the source and target domains. Additionally, it offers a reduced computational complexity, making it more efficient in practice.
This work was supported by the State Contract of Sobolev Institute of Mathematics, Project No. FWNF-2022-0015.
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References
Acharya, A., Hruschka, E.R., Ghosh, J., Acharyya, S.: Transfer learning with cluster ensembles. In: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, vol. 28, pp. 123–133 (2011)
Berikov, V.: Autoencoder-based low-rank spectral ensemble clustering of biological data. In: 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB), pp. 43–46 (2020)
Berikov, V., Pestunov, I.: Ensemble clustering based on weighted co-association matrices: error bound and convergence properties. Pattern Recognit. 63, 427–436 (2017)
Boongoen, T., Iam-On, N.: Cluster ensembles: a survey of approaches with recent extensions and applications. Comput. Sci. Rev. 28, 1–25 (2018)
Fang, Z., Lu, J., Liu, F., Zhang, G.: Semi-supervised heterogeneous domain adaptation: theory and algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 1087–1105 (2022)
Ghosh, J., Acharya, A.: Cluster ensembles. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(5), 305–315 (2011)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 1193–218 (1985)
LeCun, Y.: MNIST handwritten digit database (2013). http://yann.lecun.com/exdb/mnist
Pan, S., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2020)
Shi, Y., et al.: Transfer clustering ensemble selection. IEEE Trans. Cybern. 50(6), 2872–2885 (2018)
Shi, Y., Yu, Z., Chen, C.P., Zeng, H.: Consensus clustering with co-association matrix optimization. IEEE Transactions on Neural Networks and Learning Systems (2022)
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Berikov, V. (2024). Ensemble Clustering with Heterogeneous Transfer Learning. In: Ignatov, D.I., et al. Analysis of Images, Social Networks and Texts. AIST 2023. Lecture Notes in Computer Science, vol 14486. Springer, Cham. https://doi.org/10.1007/978-3-031-54534-4_18
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DOI: https://doi.org/10.1007/978-3-031-54534-4_18
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