A new data mining method for early warning landslides based on parallel coordinate

Wei Sun*, Shuliang Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper, a new data mining method is proposed on the basis of parallel coordinate for early warning of landslides. Landslides have resulted in many severe casualties and damaged structures and facilities. The proposed method is to analyse the landslide problems emerged with the parallel coordinates and its visualization function. It may simplify the establishment of complex model, and promote the visualization and analysis ability of spatial data, make closer relationship between spatial data and attribute data, and finally improve the effectiveness of landslide early warning.

Original languageEnglish
Title of host publicationICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services
Pages66-70
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011 - Fuzhou, China
Duration: 29 Jun 20111 Jul 2011

Publication series

NameICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Conference

Conference2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011
Country/TerritoryChina
CityFuzhou
Period29/06/111/07/11

Keywords

  • Early warning landslides
  • clustering analysis
  • data mining
  • parallel coordinates

Fingerprint

Dive into the research topics of 'A new data mining method for early warning landslides based on parallel coordinate'. Together they form a unique fingerprint.

Cite this