计算机应用与软件2018，Vol.35Issue(5)：269-272,322,5.DOI:10.3969/j.issn.1000-386x.2018.05.048
基于堆栈稀疏自编码的K-均值聚类算法的种质评价
GERMPLASM EVALUATION BASED ON STACK SPARSE SELF-ENCODING K-MEANS CLUSTERING ALGORITHM
摘要
Abstract
Aiming at the problem that a large amount of germplasm data needs to be classified in the process of constructing a database of germplasm resources,a stack sparse self-encoding K-means clustering algorithm was proposed to cluster the data.The clustering results were marked by the species quality resources with known quality, so as to achieve the purpose of classifying the quality data of the breeding data.Different from the traditional K-means clustering algorithm,the stack sparse self-encoding network was used to extract key data features.We gradually reduced the sample dimension and constructed mixed feature data as the initial center of the K-means clustering algorithm, effectively avoiding the sensitivity to the initial center selection in the K-means clustering algorithm.The experimental data showed that the accuracy of the clustering algorithm was significantly improved.关键词
聚类/堆栈稀疏自编码/种质资源/深度学习Key words
Clustering/Stack sparse self-encoding/Germplasm/Deep learning分类
信息技术与安全科学引用本文复制引用
李伟,王儒敬,贾秀芳,黄河..基于堆栈稀疏自编码的K-均值聚类算法的种质评价[J].计算机应用与软件,2018,35(5):269-272,322,5.基金项目
国家自然科学基金项目(61773360,31671586) （61773360,31671586）
中国科学院战略先导A类项目(XDA08040110). （XDA08040110）
