刘晓丽1
1.兰州理工大学电气工程与信息工程学院 ??兰州 ??730050
2.甘肃省工业过程先进控制重点实验室 ??兰州 ??730050
3.兰州理工大学国家级电气与控制工程实验教学中心 ??兰州 ??730050
基金项目:国家自然科学基金(61763029)，甘肃省基础研究创新群体基金(1506RJIA031)
详细信息
作者简介:赵小强：男，1969年生，博士生导师，教授，主要研究方向为数据挖掘、故障诊断、图像处理、污水处理、生产调度等
刘晓丽：女，1992年生，硕士生，研究方向为数据挖掘
通讯作者:赵小强 ? xqzhao@lut.cn
中图分类号:TP181计量
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被引次数:0
出版历程
收稿日期:2017-09-25
修回日期:2018-05-02
网络出版日期:2018-05-30
刊出日期:2018-08-01
An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set
Xiaoqiang ZHAO1, 2, 3,,,Xiaoli LIU1
1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Funds:The National Natural Science Foundation of China (61763029), The Gansu Province Basic Research Innovation Group Fund (1506RJIA031)
摘要
摘要:谱聚类算法通常是采用高斯核作为相似性度量，并利用所有可用的特征来构建具有欧氏距离的相似度矩阵，数据集复杂度会影响其谱聚类性能，因此该文提出一种基于公理化模糊子集(AFS)的改进谱聚类算法。首先结合AFS算法，利用识别特征来衡量更合适的数据成对相似性，生成更强大的亲合矩阵；再有效地利用Nystr?m采样算法，计算采样点间以及采样点和剩余点间的相似度矩阵去降低计算的复杂度；最后通过在不同数据集以及图像分割上进行实验，证明了提出算法的有效性。
关键词:亲和矩阵/
谱聚类/
公理化模糊子集/
Nystr?m采样算法
Abstract:Gaussian kernel is usually used as the similarity measure in spectral clustering algorithm, and all the available features are used to construct the similarity matrix with Euclidean distance. The complexity of the data set would affect its spectral clustering performance. Therefore, an improved spectral clustering algorithm based on Axiomatic Fuzzy Set (AFS) is proposed. Firstly, AFS algorithm is combined to measure the similarity of more suitable data by recognizing features, and the stronger affinity matrix is generated. Then Nystr?m sampling algorithm is used to calculate the similarity matrix between the sampling points and the sampling points and the remaining points to reduce the computational complexity. Finally, the experiment is carried out by using different data sets and image segmentations, the effectiveness of the proposed algorithm are proved.
Key words:Affinity matrix/
Spectral clustering/
Axiomatic Fuzzy Set (AFS)/
Nystr?m sampling algorithm
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