Cross Entropy Clustering Algorithm Based on Transfer Learning

  • Qing WuEmail author
  • Yu Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


To solve the problem of clustering performance degradation when traditional clustering algorithms are applied to insufficient or noisy data, a cross entropy clustering algorithm based on transfer learning is proposed. It improves the classical cross entropy clustering algorithm by combining knowledges from historical clustering centers and historical degree of membership and applying them to the objective function proposed for clustering insufficient or noisy target data. The experiment results on several synthetic and four real datasets and analyses show the proposed algorithm has high effectiveness over the available.


Cluster Cross entropy clustering Transfer learning Historical clustering center Historical degree of membership 



This work was supported in part by the National Natural Science Foundation of China under Grants (61472307, 51405387), the Key Research Project of Shaanxi Province (2018GY-018) and the Foundation of Education Department of Shaanxi Province (17JK0713).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of AutomationXi’an University of Posts & TelecommunicationsXi’anChina

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