文献类型：期刊文献

英文题名：Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

作者：Zhou, Ruihong[1];Liu, Qiaoming[2];Wang, Jian[3];Han, Xuming[4];Wang, Limin[1]

第一作者：Zhou, Ruihong

通讯作者：Liu, QM[1]

机构：[1]Jilin Univ Finance & Econ, Sch Management Sci & Informat Engn, Changchun, Peoples R China;[2]Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China;[3]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou, Peoples R China;[4]Changchun Univ Technol, Sch Comp Sci & Engn, Changchun, Peoples R China

第一机构：Jilin Univ Finance & Econ, Sch Management Sci & Informat Engn, Changchun, Peoples R China

通讯机构：[1]corresponding author), Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China.

年份：2021

卷号：33

期号：10

起止页码：4695-4712

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：;EI(收录号：20204509448517);Scopus(收录号：2-s2.0-85094824791);WOS:【SCI-EXPANDED(收录号:WOS:000584342200001)】；

基金：The work was supported by the National Science Foundation of China under Grant 61472049, 61572225 and 61202309; the key scientific research projects of colleges and universities of Henan Province (No. 21A520012), the Jilin province social science fund project (No. 2019B69), the 2018 Jilin province higher education teaching reform research project, and the 2018 Jilin university of finance and economics key project.

语种：英文

外文关键词：Semi-supervised; Affinity propagation; Fruit fly optimization alogorithm; Fuzzy density; Seismic data analysis

摘要：Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference value need to be preset, the algorithm precision could not be controlled exactly. To improve the performance of AP, this study utilizes priori pairwise constraints to obtain the reliable similarity matrix named semi-supervised affinity propagation (SAP). To find the best solution in domain of preference value, this study also proposes an improved fruit fly optimization (IFO) to optimize the unknown parameters of the SAP model. The IFO algorithm has introduced the fuzzy density mechanism to enhance the searching capacities of fruit fly individuals. The benchmark functions experiments indicate that the IFO algorithm has better precision and convergence speed than other compared swarm intelligence algorithms. We used SAP that based on IFO to identify UCI datasets and synthetic datasets. The simulation results show that proposed clustering algorithm produces significantly better clustering quality and accuracy results. In addition, we utilized the improved model to analyse the seismic data. The clustering results indicated that the proposed model had the better research potential and the good application value.