Application of Clustering Analysis in Brain Gene Data Based on Deep Learning

作     者：Suo, Yina Liu, Tingwei Jia, Xueyong Yu, Fuxing 

作者机构：North China Univ Sci & Technol Informat Engn Inst Tangshan 063000 Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063000 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：2947-2956页

核心收录：

基　　金：National Natural Science Foundation of China 

主　　题：Deep belief network fuzzy c-means algorithm unsupervised learning brain gene data clustering 

摘      要：In the current research, cluster analysis has become a very good way to obtain biological information by analyzing the brain gene expression data. In recent years, many experts have used improved traditional clustering algorithm and a new clustering algorithm to mine brain gene expression data. First, the random Forest method is used to preprocess high-dimensional and high-complexity brain gene expression data. Then, a clustering model based on deep learning is proposed, and a clustering algorithm is implemented by using deep belief network (DBN) and fuzzy c-means algorithm (FCM). This model makes full use of the generality of unsupervised learning of deep learning and clustering technology, combines the advantages of deep learning with clustering, and makes clustering effect better and more convenient for clustering high-dimensional data.