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

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.