模糊c-harmonic均值算法在不平衡数据上改进  

Improvement of fuzzy c-harmonic mean algorithm on unbalanced data

作　　者：刘富[1,2] 梁艺馨 侯涛[2] 宋阳 康冰[2] 刘云[2] LIU Fu;LIANG Yi-xin;HOU Tao;SONG Yang;KANG Bing;LIU Yun(State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China;College of Communication Engineering,Jilin University,Changchun 130022,China)

机构地区：[1]吉林大学汽车仿真与控制国家重点实验室,长春130022 [2]吉林大学通信工程学院,长春130022

出　　处：《吉林大学学报（工学版）》2021年第4期1447-1453,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51835006,61503151);吉林省青年科学基金项目(20160520100JH);中国博士后科学基金项目(2019M651204).

摘　　要：针对模糊c-harmonic均值算法(FCHM)在不平衡数据集上的聚类效果不理想的问题,提出了一种基于聚类体量约束的模糊c-harmonic均值算法。首先,利用隶属度矩阵定义各个类的体量,用于约束FCHM算法的代价函数,从而构建一个新的代价函数;然后,将该代价函数最小化,得到新的隶属度矩阵和聚类中心的计算公式;最后,在UCI数据集、模拟不平衡数据集及真实机床振动检测不平衡数据集上分别进行实验。实验结果表明,与同类算法相比,本文算法在保持传统算法全局最优性能的同时,在不平衡数据集上也能得到理想的聚类效果。A new fuzzy c-harmonic means clustering algorithm,which is based on cluster volumes constraint,is proposed in this paper to solve the problem of imperfect clustering performance of traditional algorithm for imbalanced data set.Firstly,a quantity is defined by the membership matrix to measure the volume of each cluster,which is then used to construct a new objective function by combining with that of traditional algorithm.Secondly,new membership matrix and cluster center formulas are obtained by minimizing this new objective function.The proposed algorithm was tested on the UCI data sets,simulated imbalanced data sets and actual machine vibration detection imbalanced data sets.Experimental results show that,compared with several peer algorithms,the proposed algorithm achieved good clustering performance for imbalanced data sets while maintaining the global optimal performance of the traditional one.

关 键 词：人工智能 聚类 模糊c-harmonic均值算法 全局最优 不平衡数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]