文献类型：期刊文献

英文题名：A Monte Carlo manifold spectral clustering algorithm based on emotional preference and migratory behavior

作者：Dai, Mingzhi[1,2];Feng, Xiang[1,2];Yu, Huiqun[1,2];Guo, Weibin[1];Li, Xiuquan[3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai 200237, Peoples R China;[3]Chinese Acad Sci & Technol Dev, Beijing 100038, Peoples R China

年份：2023

卷号：53

期号：16

起止页码：19742

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20231113741859);WOS:【SCI-EXPANDED(收录号:WOS:000950041900005)】；

基金：This work is supported by the National Natural Science Foundation of China (No. 62276097), Key Program of National Natural Science Foundation of China (No. 62136003), National Key Research and Development Program of China (No.2020YFB1711700), Special Fund for Information Development of Shanghai Economic and Information Commission (No. XX-XXFZ-02-20-2463) and Scientific Research Program of Shanghai Science and Technology Commission (No. 21002411-000).

语种：英文

外文关键词：Evolution intelligence; Emotional preference migration behavior clustering; Spectral clustering; Graph Laplacian; Monte Carlo simulation; Data clustering

摘要：Inspired by various behaviors of creatures in nature, numerous efficient bionic algorithms are designed for dealing with complex clustering problems. As a population-based intelligence bionic optimization model mixes individual information interaction and physical mechanism, the emotional preference and migration behavior clustering (EPMC) algorithm had proposed for dealing with clustering tasks. It is superior to multiple classic clustering algorithms and obtains epoch-making clustering effects. However, EPMC still shows premature convergence and ineffectiveness in balancing exploration and exploitation. To further enhance the searchability and the performance during clustering, we proposed a Monte Carlo spectral clustering algorithm for emotional preference and migratory behavior optimization named MCSC-EPMC. Specifically, we first incorporate the spectral clustering strategy based on Laplacian eigenmaps to assist in updating the individual. Second, a Monte Carlo statistical data theory is utilized to simulate the cluster center point and help to approach the optimal. In addition, the theoretical analysis and convergence property of MCSC-EPMC are discussed. Numerous experiments were performed to compare the proposed MCSC-EPMC with the other seven clustering algorithms on several standard datasets. And the experimental results on several standards show the effectiveness and feasibility of MCSC-EPMC. It also enhanced the clustering performance by about 6.036% compared to the EPMC.