Spatial-Temporal Scientific Data Clustering via Deep Convolutional Neural Network

Jianxin Sun, Chunxia Wu, Yufeng Ge, Yusong Li, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We explore the usage of deep convolutional neural network for clustering the time steps of a spatial-temporal scientific dataset. Our approach first takes the scientific dataset as training data and trains a deep convolutional autoencoder. A low-dimensional feature space or latent space can be extracted by inferencing the encoding part of the network. As a result, each time step is transformed into a feature descriptor that can be compared with each other in the feature space. In this way, we can cluster time steps according to their feature descriptors, and each group of time steps has a similar characterization. We demonstrate the effectiveness of our approach using a real-world simulation dataset of water contamination. Multiple variables and their combinations of this dataset are fed into our approach. The trained network enables the clustering of the time steps and facilitates scientists to examine their large spatial-temporal datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3424-3429
Number of pages6
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period12/9/1912/12/19

Keywords

  • autoencoder
  • clustering
  • deep convolutional neural network
  • feature descriptor
  • spatial-temporal scientific data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Spatial-Temporal Scientific Data Clustering via Deep Convolutional Neural Network'. Together they form a unique fingerprint.

  • Cite this

    Sun, J., Wu, C., Ge, Y., Li, Y., & Yu, H. (2019). Spatial-Temporal Scientific Data Clustering via Deep Convolutional Neural Network. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 3424-3429). [9006507] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006507