计算机应用与软件2024,Vol.41Issue(3) :266-275.DOI:10.3969/j.issn.1000-386x.2024.03.041

基于广义粒度自编码器的模糊粗糙聚类方法

FUZZY ROUGH CLUSTERING METHOD BASED ON GENERALIZED GRANULARITY SELF ENCODER

李繁 张晓宇 刘林东
计算机应用与软件2024,Vol.41Issue(3) :266-275.DOI:10.3969/j.issn.1000-386x.2024.03.041

基于广义粒度自编码器的模糊粗糙聚类方法

FUZZY ROUGH CLUSTERING METHOD BASED ON GENERALIZED GRANULARITY SELF ENCODER

李繁 1张晓宇 1刘林东2
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作者信息

  • 1. 新疆财经大学网络与实验教学中心 新疆乌鲁木齐 830012
  • 2. 新疆财经大学会计学院 新疆乌鲁木齐 830012
  • 折叠

摘要

为了解决模糊化器参数的不确定性问题,提出基于广义粒度自编码器的模糊粗糙聚类方法.基于阴影集优化每个聚类的划分阈值,将所有模式划分为不同的近似区域;通过多粒度近似区域捕捉模糊参数产生的不确定性,包括模糊化系数产生的不确定性,边界区域和重叠分区产生的模糊性;进一步建立多级粒度自编码器评价聚类模型的质量.多个数据集聚类对比实验表明该方法能够有效挖掘不确定信息,提升聚类性能.

Abstract

In order to solve the uncertainty of fuzzier parameter,a fuzzy rough clustering method based on generalized granularity self-encoder is proposed.The segmentation threshold of each cluster was optimized based on shadow set,and all patterns were divided into different approximate regions.The multiple granularity approximation region was used to capture the uncertainties caused by fuzzy parameters,including the uncertainty caused by the fuzzy coefficient,the fuzziness generated by the boundary region and overlapping regions.Furthermore,a multiple level granularity self-encoder was established to evaluate the quality of the clustering model.The experimental results on multiple datasets show that this method can effectively mine uncertain information and improve the clustering performance.

关键词

多粒度/聚类/阴影集/模糊

Key words

Multiple granularity/Clustering/Shadow set/Fuzzy

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基金项目

新疆维吾尔自治区高等学校科研项目(XJEDU2013I26)

出版年

2024
计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
参考文献量15
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