A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

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dc.contributor.authorJiang, Zhenni
dc.contributor.authorLiu, Xiyu
dc.contributor.authorSun, Minghe
dc.creator.orcidhttps://orcid.org/0000-0001-8503-9761en_US
dc.date.accessioned2023-04-03T16:37:56Z
dc.date.available2023-04-03T16:37:56Z
dc.date.issued2019-07-31
dc.description.abstractThis study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.en_US
dc.description.departmentManagement Science and Statisticsen_US
dc.description.sponsorshipNational Natural Science Foundation of China; Social Science Fund Project of Shandong; China Postdoctoral Science Foundation Funded Project; Natural Science Foundation of the Shandong Provincial; China Postdoctoral Special Funding Project; Youth Fund for Humanities and Social Sciences, Ministry of Educationen_US
dc.identifier.citationJiang, Z., Liu, X., & Sun, M. (2019). A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. Mathematical Problems in Engineering, 2019, 1713801. doi:10.1155/2019/1713801en_US
dc.identifier.issn1563-5147
dc.identifier.otherhttps://doi.org/10.1155/2019/1713801
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1807
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.ispartofseriesMathematical Problems in Engineering, 2019;
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.titleA Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P Systemen_US
dc.typeArticleen_US

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