A novel spatio-temporal data analysis approach based on prospective support vector clustering

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

Spatio-temporal data mining has recently gained considerable attention from both the research and practitioner communities. In this paper, we propose a novel spatio-temporal data analysis approach which is aimed at discovering abnormal spatial clustering patterns in a timely manner. Our approach is based on a robust clustering engine using support vector machines and incorporates the ideas from existing online surveillance methods to monitor for incremental changes over time. Two simulated scenarios are created to evaluate our approach. Initial experimental results indicate that our approach is able to detect abnormal areas with irregular shapes faster and more accurately than a widely-used spatio-temporal data analysis approach based on scan statistics.

Original languageEnglish (US)
Pages129-134
Number of pages6
StatePublished - Jan 1 2005
Event15th Workshop on Information Technology and Systems, WITS 2005 - Las Vegas, NV, United States
Duration: Dec 10 2005Dec 11 2005

Other

Other15th Workshop on Information Technology and Systems, WITS 2005
CountryUnited States
CityLas Vegas, NV
Period12/10/0512/11/05

    Fingerprint

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering

Cite this

Chang, W., Zeng, D., & Chen, H. (2005). A novel spatio-temporal data analysis approach based on prospective support vector clustering. 129-134. Paper presented at 15th Workshop on Information Technology and Systems, WITS 2005, Las Vegas, NV, United States.