Work place: College of Electronic Science and Technology, National University of Defense Technology, Changsha, China
Research Interests: Computational Complexity Theory, Detection Theory
Zenghui Hu was born in Pingxiang, Jiangxi province, in 1982. He obtained his bachelor degree of science and Master Degree of science in 2004 and 2006, respectively, from National University of Defense Technology, Changsha, Hunan, China.
He is now a Ph.D. candidate in the College of Electrical Science and Engineering, National University of Defense Technology. His research interests include blind source separation, array signal processing, evidence theory etc.
DOI: https://doi.org/10.5815/ijigsp.2011.01.05, Pub. Date: 8 Feb. 2011
Clustering belief functions is not easy because of uncertainty and the unknown number of clusters. To overcome this problem, we extend agglomerative algorithm for clustering belief functions. By this extended algorithm, belief distance is taken as dissimilarity measure between two belief functions, and the complete-link algorithm is selected to calculate the dissimilarity between two clusters. Before every merging of two clusters, consistency test is executed. Only when the two clusters are consistent, they can merge, otherwise, dissimilarity between them is set to the largest value, which prevents them from merging and assists to determine the number of final clusters. Typical illustration shows same promising results. Firstly, the extended algorithm itself can determine the number of clusters instead of needing to set it in advance. Secondly, the extended algorithm can deal with belief functions with hidden conflict. At last, the algorithm extended is robust.[...] Read more.
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