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Multi-View Dynamic Kernelized Evidential Clustering

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  • 国家科技期刊平台
  • NETL
  • NSTL
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Multi-View Dynamic Kernelized Evidential Clustering
It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view cluster-ing methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernel-ized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall perfor-mance.

Evidential clusteringimprecision characterizingkernel techniquemulti-view clustering

Jinyi Xu、Zuowei Zhang、Ze Lin、Yixiang Chen、Weiping Ding

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Software Engineering Institute,East China Normal University,Shanghai 200062

Shanghai Key Laboratory of Trustworthy Computing,East China Normal University,Shanghai 200062,China

School of Automation,Northwestern Polytechnical University,Xi'an 710072,China

School of Artificial Intelligence and Computer Science,Nantong University,Nantong 226019

Faculty of Data Science,City University of Macau,Macau 999078,China

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Evidential clustering imprecision characterizing kernel technique multi-view clustering

2024

10.1109/JAS.2024.124608

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(12)