计算机与数字工程2024，Vol.52Issue(12)：3686-3689,4.DOI:10.3969/j.issn.1672-9722.2024.12.036
基于改进Canopy的FCM聚类算法
FCM Clustering Algorithm Based on Improved Canopy
孙偲远 1马鑫海1
作者信息
- 1. 江苏科技大学计算机学院 镇江 212100
- 折叠
摘要
Abstract
The traditional FCM clustering algorithm relies on the initial clustering center.According to the characteristics of randomly selecting the initial clustering center,it is easy to cause the objective function to fall into the local optimal solution,and the convergence speed of large-scale data sets is slow.This paper proposes a density based canopy algorithm to improve the original FCM algorithm,so as to automatically select the initial clustering center,and take the selected clustering center as the input of FCM algorithm,in order to speed up the convergence of the algorithm and reduce the inaccurate accuracy of the classification re-sults caused by random selection.关键词
聚类算法/聚类中心/FCM算法/密度Canopy算法Key words
clustering algorithm/cluster center/FCM algorithm/density Canopy algorithm分类
信息技术与安全科学引用本文复制引用
孙偲远,马鑫海..基于改进Canopy的FCM聚类算法[J].计算机与数字工程,2024,52(12):3686-3689,4.
