Rough Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Weights

ZHANG Qiang; WANG Hong-wei; CHEN You; WANG Wenzhe

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Rough Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Weights

更新时间：2026-09-04

- Rough Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Weights
- Vol. 33, Issue 12, Pages: 80-84(2016)
- 作者机构：
  
  空军工程大学机构航空航天工程学院,陕西,西安,710038
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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张强, 王红卫, 陈游, 王文哲. 基于自适应权重的RFCM聚类算法[J]. 微电子学与计算机, 2016, 33(12): 80-84.

ZHANG Qiang, WANG Hong-wei, CHEN You, et al. Rough Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Weights[J]. 2016, 33(12): 80-84.
张强, 王红卫, 陈游, 王文哲. 基于自适应权重的RFCM聚类算法[J]. 微电子学与计算机, 2016, 33(12): 80-84. DOI：

ZHANG Qiang, WANG Hong-wei, CHEN You, et al. Rough Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Weights[J]. 2016, 33(12): 80-84. DOI：

摘要

提出了基于自适应权重的RFCM聚类算法

主要思路是在每一次迭代中

根据每个对象与聚类中心欧式距离不同

选择改进的正半轴反余切函数对欧式距离进行重新分布后确定均衡因子

通过均衡因子动态地调节固定权重获得自适应权重

进而运行RFCM算法.最后

基于人工和UCI数据集的仿真验证了所提算法的有效性

并应用于雷达信号分选

验证了所提算法的实用性.

Abstract

This paper proposed RFCM clustering algorithm based on self-adaptive weights. According to different distance between every data object and clustering center

using improved arc cotangent function to redistribute the distance

acquiring adaptive weights through the equivalence factor in each iteration

then carrying out RFCM clustering algorithm. The simulations are performed on synthetic and UCI data sets

and the results show the validity of the proposed algorithm. Furthermore

the proposed algorithm is applied in sorting radar signal

and the results show the practicability of the proposed algorithm.

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references

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