吉林大学学报（理学版）Issue(4)：705-709,5.DOI:10.13413/j.cnki.jdxblxb.2015.04.21
基于半监督的模糊 C-均值聚类算法
Fuzzy C-Means Clustering Algorithm Based on Semi-supervised Learning
摘要
Abstract
A fuzzy C-means clustering algorithm based on semi-supervised learning was proposed by introducing semi-supervised learning into fuzzy C-means clustering algorithm.It has effectively solved the problem that the initial clustering centers random selection of fuzzy C-means algorithm can easily cause the local convergence and affects the clustering.The proposed algorithm can objectively obtain the optimal number of clusters and the initial cluster centers.Compared with the traditional FCM, our method can reduce the number of iterations and the dependence on initial cluster centers.关键词
半监督学习/模糊 C-均值聚类算法/信息熵Key words
semi-supervised learning/fuzzy C-means clustering algorithm/information entropy分类
信息技术与安全科学引用本文复制引用
郭新辰,郗仙田,樊秀玲,韩啸..基于半监督的模糊 C-均值聚类算法[J].吉林大学学报（理学版）,2015,(4):705-709,5.基金项目
国家自然科学基金(批准号:11226263 （）
11201057 （）
61202261)、吉林省自然科学基金(批准号:201215165)和吉林省高校科学技术研究计划项目(批准号:2015(248)) （批准号:201215165）
