[1]张 曦,李 璠,付雪峰,等.随机学习萤火虫算法优化的模糊软子空间聚类算法[J].江西师范大学学报(自然科学版),2021,(02):137-144.[doi:10.16357/j.cnki.issn1000-5862.2021.02.05]
　ZHANG Xi,LI Fan,FU Xuefeng,et al.The Fuzzy Soft Subspace Clustering Algorithm Optimized by Random Learning Firefly Algorithm[J].Journal of Jiangxi Normal University:Natural Science Edition,2021,(02):137-144.[doi:10.16357/j.cnki.issn1000-5862.2021.02.05]
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随机学习萤火虫算法优化的模糊软子空间聚类算法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年02期
页码:
137-144
栏目:
信息科学与技术
出版日期:
2021-04-10

文章信息/Info

Title:
The Fuzzy Soft Subspace Clustering Algorithm Optimized by Random Learning Firefly Algorithm
文章编号:
1000-5862(2021)02-0137-08
作者:
张 曦1; 李 璠1; 2; 付雪峰1; 2; 谭德坤1; 2; 赵 嘉1; 2; 3*
1.南昌工程学院信息工程学院,江西 南昌 330099; 2.江西省水信息协同感知与智能处理重点实验室,江西 南昌 330099; 3.鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室,江西 南昌 330099
Author(s):
ZHANG Xi1; LI Fan1; 2; FU Xuefeng1; 2; TAN Dekun1; 2; ZHAO Jia1; 2; 3*
1.School of Information Engineering,Nanchang Institute of Technology,Nanchang Jiangxi 330099,China; 2.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Jiangxi 330099,China; 3.National-Local Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area,Nanchang Jiangxi 330099,China
关键词:
软子空间聚类;  变量加权;  萤火虫算法
Keywords:
soft subspace clustering;  variable weighting;  firefly algorithm
分类号:
TP 301.6
DOI:
10.16357/j.cnki.issn1000-5862.2021.02.05
文献标志码:
A
摘要:
传统软子空间聚类算法在利用局部搜索策略解决等式约束的连续非线性的变量加权问题时,易陷入局部最优导致聚类效果不佳.针对该问题,该文提出了一种随机学习萤火虫算法优化的模糊软子空间聚类算法.该算法利用具有全局搜索能力的萤火虫算法对新算法的目标函数进行优化,同时,为弥补萤火虫算法易提前收敛和寻优精度较低的缺陷,对萤火虫种群进化方式和全局最优粒子的学习方式进行了改进.新算法将权值矩阵拟化成萤火虫种群,使变量加权的等式约束变为界约束,通过萤火虫位置的更新搜索最优权重并发掘子空间中隐藏的簇类.在人工数据集、UCI标准数据集和癌症基因表达数据集上的实验结果表明:该算法具有较好的聚类效果.
Abstract:
The traditional soft subspace clustering algorithm uses local search strategy to solve the continuous nonlinear variable weighting problem with equality constraints,and is easy to fall into local optimum,resulting in poor clustering result.To solve this problem,a fuzzy soft subspace clustering algorithm optimized by random learning firefly algorithm is proposed.The firefly algorithm with global search ability is used to optimize the objective function of the new algorithm.At the same time,in order to make up for premature convergence and low precision of firefly algorithm,the evolution pattern of firefly population and the learning method of global optimal particle are improved.The new algorithm formulates the weight matrix into firefly population,and transforms equality constraints of variable weighting problem into bound constraints,updating the firefly position to search for optimal weight and to explore the hidden clusters in the subspace.The experimental results on artificial dataset,UCI standard dataset and cancer gene expression dataset show that the new algorithm has better clustering effect.

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备注/Memo

备注/Memo:
收稿日期:2020-01-18
基金项目:国家自然科学基金(52069014,61762063,51669014),江西省自然科学基金(2018ACB21029)和江西省教育厅科学技术研究(GJJ190956)资助项目.
通信作者:赵 嘉(1981—),男,安徽桐城人,教授,博士,主要从事智能计算与计算智能、数据挖掘与机器学习等研究.E-mail:zhaojia925@163.com
更新日期/Last Update: 2021-04-10