杨程宇1,
王志豪1,
罗斯哲1,
潘金艳2,,
1.厦门大学航空航天学院 ??厦门 ??361102
2.集美大学信息工程学院 ??厦门 ??361021
基金项目:国家自然科学基金(61203176)，福建省自然科学基金(2013J05098, 2016J01756)
详细信息
作者简介:高云龙：男，1979年生，副教授，研究方向为机器学习、时间序列分析和生产制造系统优化与调度
杨程宇：男，1996年生，本科生，研究方向为机器学习
王志豪：男，1993年生，硕士生，研究方向为模式识别和机器学习
罗斯哲：男，1995年生，硕士生，研究方向为维数约简、模式识别和机器学习
潘金艳：女，1978年生，副教授，研究方向为人工智能和机器学习理论与方法
通讯作者:潘金艳 gaoyl@xmu.edu.cn
中图分类号:TP311.13计量
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被引次数:0
出版历程
收稿日期:2018-06-20
修回日期:2018-12-24
网络出版日期:2018-12-28
刊出日期:2019-05-01
Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information
Yunlong GAO1,Chengyu YANG1,
Zhihao WANG1,
Sizhe LUO1,
Jinyan PAN2,,
1. School of Aerospace Engineering, Xiamen University, Xiamen 361102, China
2. Information Engineering College, Jimei University, Xiamen 361021, China
Funds:The National Natural Science Foundation of China (61203176), The Natural Science Foundation of Fujian Province (2013J05098, 2016J01756)
摘要
摘要:与经典的K均值聚类算法相比，模糊C均值(FCM)聚类算法通过引入模糊因子，考虑不同聚类数据簇之间的相互关系，得到可分性更好的聚类结果。但是模糊因子的引入，使得任意一个样本点都存在模糊性，造成FCM极易受到噪声和离群点的影响，聚类结果泛化性能较差。因此，该文提出一种簇间可分的鲁棒FCM算法(RBI-FCM)。RBI-FCM利用K均值算法对模糊隶属度的稀疏特征，降低不同数据簇之间的相互作用，突出不同数据簇相邻区域的可分性；另外，RBI-FCM在极小化数据簇内部散布度的条件下，考虑不同数据簇之间的可分性，可提高聚类模型的泛化性能。该文设计了有效的模型求解迭代算法。实验结果表明，RBI-FCM算法提高了FCM的鲁棒性，有效降低FCM对数据簇分布差异性和抽样不均衡的敏感性，得到理想的聚类结果。
关键词:聚类/
模糊C均值/
样本分布/
簇间信息
Abstract:Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.
Key words:Clustering/
Fuzzy C-Means (FCM)/
Sample distribution/
Between-cluster information
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