摘要
为解决最小二乘孪生支持向量机聚类中心平面无限延伸影响聚类性能问题,将样本类质心融入目标函数和聚类中心平面构建中,提出局部最小二乘孪生支持向量机聚类算法。该算法充分利用局部信息,通过求解一系列线性方程组,交替更新聚类中心平面和类质心,有效提升最小二乘孪生支持向量机聚类算法性能。在合成数据集与现实数据集上进行试验,结果说明所提算法的有效性。
In order to solve the problem that the infinite extension of cluster center plane may affect the clustering performance of least squares twin support vector machine,the cluster centroid of sample points is integrated into the construction of objective function and cluster center plane,and a local least squares twin support vector machine clustering algorithm is proposed.The algorithm makes full use of local information,solves a series of linear equations,alternately updates the cluster center plane and the cluster centroid,and effectively improves the performance of the least squares twin support vector machine clustering algorithm.Experiments on both synthetic datasets and real-world datasets validate the effectiveness of the proposed algorithm.
作者
黄凤如
陈素根
HUANG Fengru;CHEN Sugen(School of Mathematics and Physics,Anqing Normal University,246133,Anqing,Anhui,China;Key Laboratory of Modeling,Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes,246133,Anqing,Anhui,China)
出处
《淮北师范大学学报(自然科学版)》
2025年第4期61-69,共9页
Journal of Huaibei Normal University(Natural Sciences)
基金
国家自然科学基金项目(61702012)
安徽省高等学校自然科学研究重点项目(2024AH051095)。
关键词
机器学习
最小二乘孪生支持向量机
聚类
平面聚类
局部信息
machine learning
least squares twin support vector machine
clustering
plane clustering
local information
