UM
K-medoids method based on divergence for uncertain data clustering
Zhou J.1; Pan Y.1; Chen C.L.P.2; Wang D.1; Han S.1
2017-02-06
Source Publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Pages2671-2674
AbstractUncertain data clustering is an essential task in the research of data mining. Lots of traditional clustering methods are extended with new similarity measurements to tackle this issue. Different from certain data clustering, uncertain data clustering focus more on the evaluation of distribution similarity between uncertain data objects. In this paper, based on the KL-divergence and the JS-divergence, we propose a novel K-medoids method for clustering uncertain data, named UK-medoids. Good performance of the proposed algorithm is shown in experiments on synthetic datasets.
KeywordJS-divergence K-medoids method KL-divergence Uncertain data clustering
DOI10.1109/SMC.2016.7844643
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.University of Jinan
2.Universidade de Macau
Recommended Citation
GB/T 7714
Zhou J.,Pan Y.,Chen C.L.P.,et al. K-medoids method based on divergence for uncertain data clustering[C],2017:2671-2674.
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