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An Evidence Combination Method based on DBSCAN Clustering

Kehua Yang1,2,*, Tian Tan1, Wei Zhang1

College of Computer Science and Electronic Engineering, and Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha, Hunan, 410082, China.
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Virginia, Blacksburg, 24060, USA.

* Corresponding Author: Kehua Yang. Email: email.

Computers, Materials & Continua 2018, 57(2), 269-281.


Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two different discount factors are adopted to revise the original evidence sets, based on the result of DBSCAN density clustering. Finally, we conduct the information fusion for the revised evidence sets by D-S combination rules. Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence, with better accuracy, stability and convergence speed.


Cite This Article

APA Style
Yang, K., Tan, T., Zhang, W. (2018). An evidence combination method based on DBSCAN clustering. Computers, Materials & Continua, 57(2), 269-281.
Vancouver Style
Yang K, Tan T, Zhang W. An evidence combination method based on DBSCAN clustering. Comput Mater Contin. 2018;57(2):269-281
IEEE Style
K. Yang, T. Tan, and W. Zhang "An Evidence Combination Method based on DBSCAN Clustering," Comput. Mater. Contin., vol. 57, no. 2, pp. 269-281. 2018.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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