一种邻域粒的模糊C均值聚类算法
A neighbourhood granular fuzzy C-means clustering algorithm
郑晨颖 1陈颖悦 2侯贤宇 1江连吉 1廖亮1
作者信息
- 1. 厦门理工学院计算机与信息工程学院,福建 厦门 361024
- 2. 厦门理工学院计算机与信息工程学院,福建 厦门 361024;厦门理工学院经济与管理学院,福建 厦门 361024
- 折叠
摘要
针对初始值和噪声的敏感性会导致模糊C均值聚类效果下降这一问题,引入粒计算理论,采用邻域粒化技术,提出邻域粒模糊C均值聚类算法.样本在单特征上使用邻域粒化技术构造邻域粒子,在多特征上粒化形成邻域粒向量,定义多种粒距离公式度量粒子之间的距离.根据粒距离度量,提出粒模糊C均值聚类算法,采用多个数据集进行实验,将粒模糊C均值聚类算法与经典聚类算法进行比较,验证了所提出的邻域粒模糊C均值聚类算法的可行性和有效性.
Abstract
Aiming at the problem that the sensitivity of initial value and noise lead to the decline of fuzzy C-means clustering,fuzzy C-means clustering method of neighborhood granule is proposed by introducing the theory of granular computation and using the neighborhood granulation technique.In the sample,the neighborhood granule is constructed by using the neighborhood granulation technique on single feature,and the neighborhood granular vector is formed by using granulation on multi-features.A variety of granule distance formulas are defined to measure the distance between granules.According to the granule distance measurement,a granular fuzzy C-means clustering method is proposed,and a granular fuzzy C-means clustering algorithm is designed.Multiple data sets are used to perform experiments,and the fuzzy C-means clustering algorithm is compared with the classical clustering algorithm.The results verify the feasibility and effectiveness of the proposed neighborhood granular fuzzy C-means clustering method.
关键词
粒计算/邻域粒/模糊C均值聚类/无监督模糊聚类方法/粒向量Key words
granular computing/neighbourhood granules/fuzzy C-means clustering/unsupervised fuzzy clustering method/granule vectors引用本文复制引用
基金项目
国家自然科学基金(61976183)
厦门市科技计划(2022CXY0428)
出版年
2024
NETL
NSTL
万方数据