摘要
传统模糊??-均值(FCM)算法要求一个样本对于各个聚类的隶属度之和满足归一化条件,从而导致算法对噪声和孤立点敏感,对非均衡分布样本的聚类有效性降低.针对该问题,提出一种改进模糊隶属函数约束的FCM聚类算法,通过放松归一化条件,推导出新的隶属度划分公式,并在聚类过程中不断进行隶属度修正,从而达到消除噪声样本、提高聚类有效性的目的.最后通过实验结果对比验证了改进算法的正确性.
Since the general fuzzy C-means（FCM） algorithm requires sum of membership satisfying the normalization condition for a sample to each cluster, and thus results algorithm sensitive to noise or outliers and reducing the validity of the clustering on non-equilibrium distribution samples. Therefore, an FCM clustering algorithm with the improved fuzzy membership constraint function is proposed. By relaxing the normalization condition, a new formula of membership division is deduced, and the membership is constantly corrected in the clustering process, so that it will eliminate the noise sample,and improve the validity of clustering. Finally, the comparison of the experimental result verifies the correctness of the improved algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第12期2270-2274,共5页
Control and Decision
基金
湖南省自然科学基金项目(2015JJ2047
13JJ9031)
湖南工业大学自然科学基金项目(2014HZX29)
湖南省教育厅项目(12C0074)
