Abstract
Classical prototype-based clustering algorithms usually cannot achieve satisfactory results when the data is insufficient. Transfer learning can be adopted to address this problem. For instance, in the recently proposed transfer clustering methods Transfer Evidential C-Means (TECM), the prototypes of data in the source domain are transferred to the target domain to help improve the clustering performance. However, in TECM the prototypes are calculated based on all the features of samples in the clusters in source data sets. Due to distribution shift in two domains, sometimes the prototypes obtained from all the features of samples in the source may not be a good representation for clusters in the target domain. In this paper, we propose an approach for solving this problem by exploiting causal inference, and introduce a new prototype-based causal transfer evidential clustering algorithm. The experimental results demonstrate the effectiveness of the proposed clustering approach.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61701409), the Aero Science Foundation of China (No. 20182053023), the Science Research Plan of China (Xi’an) Institute for Silk Road Research (2019ZD02).
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Zhou, K., Jiang, M. (2022). Causal Transfer Evidential Clustering. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_2
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