改进的最小二乘孪生支持向量机聚类  

Improved Least Squares Twin Support Vector Clustering

作　　者：刘玉菲 陈素根 LIU Yufei;CHEN Sugen(School of Mathematics and Physics,Anqing Normal University,Anqing 246133,China)

机构地区：[1]安庆师范大学数理学院,安徽安庆246133

出　　处：《安庆师范大学学报（自然科学版）》2022年第3期12-19,共8页Journal of Anqing Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61702012);安徽省高校自然科学研究重点项目(KJ2020A0505);安徽省自然科学基金项目(1908085MF195,2008085MF193)。

摘　　要：聚类是模式识别与机器学习领域的基本问题之一,孪生支持向量机聚类是基于平面聚类的新算法,它没有考虑数据分布对聚类效果的影响。为了解决这个问题,本文考虑其余类样本点分布对聚类平面的影响,构造了一种新的权重函数,并给予样本点不同的惩罚,从而降低离群点对聚类效果的影响。在此基础上,提出一种改进的最小二乘孪生支持向量机聚类算法,该算法只需要求解一系列线性方程组问题,无需求解二次规划问题。人工数据集和UCI数据集上的实验验证了所提算法的有效性。Clustering is one of the basic problems in the field of pattern recognition and machine learning.Twin support vector clustering is a new algorithm based on plane clustering,which does not consider the effect of data distribution on clus-tering.In order to solve this problem,this paper considers the influence of the distribution of sample points of other classes to the clustering plane,and then constructs a new weight function which gives different punishment to sample points to reduce the influence of outliers on the clustering results.On this basis,an improved least squares twin support vector clustering algo-rithm is proposed,which only requires solving a series of linear system of equations without solving the quadratic program-ming problem.Experiments are conducted on some artificial data sets and UCI data sets,and the results verify the effective-ness of the proposed algorithm.

关 键 词：模式识别 聚类 最小二乘孪生支持向量机 权重函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]