A Data Fusion Algorithm Based on Clustering Evidence Theory

  • Yuchen WangEmail author
  • Wenqing Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


Based on the idea of clustering and evidence theory, a new measurement data fusion algorithm is proposed. Firstly, all the measured values are clustered into groups according to the hierarchical clustering method, the best clustering group is selected, and each group is given different weights. Secondly, the set consisting of each group of measured values is regarded as identification framework, then the measured values in the group are converted into the corresponding evidence, which is fused after the evidence is modified, and the fused evidence is regarded as the weight of each measured value. Finally, after the data is weighted and summed within the group, weighted summation between groups to obtain the fusion result. The validity of the method is verified by the data simulation.


Data fusion Hierarchical clustering Evidence theory 



This work is supported by Shaanxi Provincial Education Department industrialization project (16JF024) and is the key project in the field of industry (2018ZDXM-GY-039).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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