JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (1): 59-66.doi: 10.6040/j.issn.1671-9352.0.2021.671

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Density-sensitive spectral clustering based on three-way decision

FAN Jia-chen1, WANG Ping-xin2*, YANG Xi-bei1   

  1. 1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China;
    2. School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China
  • Published:2023-02-12

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Abstract: This paper integrates density sensitive spectral clustering with three-way decision and presents a density sensitive spectral clustering algorithm based on three-way decision. In the proposed algorithm, the upper bound of each cluster is obtained by introducing tolerance parameters in the process of density-sensitive spectral clustering, and the core is separated from the upper bound by the perturbation analysis algorithm. The difference between the upper bound and the core region is regarded as the fringe region of the specific cluster. The clustering result uses the core region and the fringe region to represent each cluster, which can more comprehensively display the data structure. Compared with the experimental results of the traditional hard clustering algorithm on the UCI data set, the proposed algorithm is effective in improving the value of AS and ACC and reducing the value of DBI by using the core region to calculate the evaluation indicators of clustering, which indicates that the proposed algorithm can be used to solve the problem of clustering uncertain objects.

Key words: three-way decision, three-way clustering, density-sensitive, similarity measure

CLC Number: 

  • TP301.6
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