An Autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data
Currently,there has been a dramatic growth of data size and data dimension in geophysics,while achieving the rapid advancements of geological big data technologies with the support of various detection methods.Then,high-dimensional and massive geological data impose very challenging obstacles to traditional data analysis approaches.Given the success of deep learning methods and techniques in big data analysis applications,it is expected that they are also able to achieve the satisfactory performance in dealing with high-dimensional complex geological data.Hence,through the combination of one of the effective implementations of deep learning,i.e.,autoencoder,and a clustering algorithm,i.e.,K-means,in this paper we achieve the dimensionality reduction for complex data,so as to extract useful data features from mineral deposit data,with the purpose of improving computational efficiency.The experimental results demonstrate the effectiveness our developed method.
High-dimensional data Dimensionality reduction Autoencoder K-means algorithm
Yan Li Xiong Luo Maojian Chen Yueqin Zhu Yang Gao
School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijin Development and Research Center,China Geological Survey,Beijing 100037,China China Information Technology Security Evaluation Center,Beijing 100085,China
国际会议
江苏镇江
英文
408-415
2019-09-20（万方平台首次上网日期，不代表论文的发表时间）