Detecting graph-based spatial outliers

  • Authors:

  • Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • Affiliations:

  • Computer Science Department, University of Minnesota, 200 Union Street SE, Minneapolis, MN-55455, USA. Tel.: +1 612 6248307/ Fax: +1 612 6250572/ E-mail: {shekhar,ctlu,pusheng}@cs.umn.edu;(Correspond. Tel.: +1 612 626 7703/ E-mail: ctlu@cs.umn.edu) Computer Science Department, University of Minnesota, 200 Union Street SE, Minneapolis, MN-55455, USA. Tel.: +1 612 6248307/ Fax: +1 61 ...;Computer Science Department, University of Minnesota, 200 Union Street SE, Minneapolis, MN-55455, USA. Tel.: +1 612 6248307/ Fax: +1 612 6250572/ E-mail: {shekhar,ctlu,pusheng}@cs.umn.edu

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2002

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Abstract

Identification of outliers can lead to the discovery of unexpected and interesting 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 a fast algorithm to detect spatial outliers, and provide cost models for outlier detection procedures. In addition, we provide experimental results from the application of our algorithm on a Minneapolis-St. Paul (Twin Cities) traffic data set to show its effectiveness and usefulness.