Detecting graph-based spatial outliers: Algorithms and applications(a summary of results)

Shashi Shekhar, Chang Tien Lu, Pusheng Zhang

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

107 Scopus citations

Abstract

Identification of outliers can lead to the discovery of unexpected, interesting, and useful knowledge. Existing methods are designed for detecting Spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define statistical tests, analyze the statistical foundation underlying our approach, design several fast algorithms to detect spatial outliers, and provide a cost model for outlier detection procedures. In addition, we provide experimental results from the application of our algorithms on a Minneapolis-St. Paul(Twin Cities) traffic dataset to show their effectiveness and usefulness.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsF. Provost, R. Srikant, M. Schkolnick, D. Lee
Pages371-376
Number of pages6
StatePublished - Dec 1 2001
EventProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - San Francisco, CA, United States
Duration: Aug 26 2001Aug 29 2001

Other

OtherProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)
CountryUnited States
CitySan Francisco, CA
Period8/26/018/29/01

Keywords

  • Outlier Detection
  • Spatial Data Mining
  • Spatial Graphs

Fingerprint

Dive into the research topics of 'Detecting graph-based spatial outliers: Algorithms and applications(a summary of results)'. Together they form a unique fingerprint.

Cite this