计算机工程与应用Issue(4)：154-157,4.DOI:10.3778/j.issn.1002-8331.1402-0149
基于堆叠稀疏自编码的模糊C-均值聚类算法
Fuzzy C-means clustering algorithm based on stacked sparse autoencoders Computer Engineering and Applications
摘要
Abstract
In order to solve the sensitivity of fuzzy C-means clustering algorithm to the outlier and the randomly initialized clustering center, the stacked sparse autoencoders and traditional fuzzy C-means clustering algorithm are combined to improve the traditional fuzzy C-means clustering algorithm. Because the stacked sparse autoencoders can extract features of the original data set from low-level to high-level, and high-level features can reflect the nature features of the sample data to be clustered better than the original data set, which will help to improve the clustering effect with high-level features instead of the original data. With experimenting on several standard data sets of UCI, it is shown that the improved algorithm is feasible.关键词
堆叠稀疏自编码/模糊C-均值聚类/特征/深度学习Key words
stacked sparse autoencoders/fuzzy C-means clustering/features/deep learning分类
信息技术与安全科学引用本文复制引用
段宝彬,韩立新,谢进..基于堆叠稀疏自编码的模糊C-均值聚类算法[J].计算机工程与应用,2015,(4):154-157,4.基金项目
江苏省高校“青蓝工程”中青年学术带头人培养对象资助项目；安徽省自然科学基金项目（No.1208085MA15）；合肥学院应用数学重点建设学科基金（No.2014xk08）。 （）
