吉林大学学报（理学版）Issue(6)：1293-1296,4.DOI:10.13413/j.cnki.jdxblxb.2014.06.35
改进的 FCM 半监督聚类算法
Improved Fuzzy C-Means Clustering Algorithm
摘要
Abstract
A new fuzzy C-means clustering algorithm was proposed by the introduction of functions of separation between clusters into FCM clustering algorithm and with the nature of semi-supervised learning considered.The model of semi-supervised FCM clustering algorithm with the information entropy as constraints was established and the solution to the model was derived.The simulation experiments were performed on UCI data sets to verify the effectiveness of the proposed algorithm. The experimental results show that this modified algorithm gets the better validity and performance.关键词
半监督聚类/模糊 C-均值算法/信息熵Key words
semi-supervised clustering/fuzzy C-means algorithm (FCM)/information entropy分类
信息技术与安全科学引用本文复制引用
郭新辰,樊秀玲,郗仙田,韩啸..改进的 FCM 半监督聚类算法[J].吉林大学学报（理学版）,2014,(6):1293-1296,4.基金项目
国家自然科学基金(批准号:11226263 （）
11201057 （）
61202261)和吉林省自然科学基金(批准号:201215165) （批准号:201215165）
