Detecting Graph-Based Spatial Outliers: Algorithms and Applications

Abstract

Identification of outliers can lead to the discovery of unexpected interesting and implicit 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 tests for spatial outliers in graph structured data sets analyze the statistical foundation underlying our approach design a fast algorithm to detect spatial outliers provide the cost model for outlier detection procedures In addition we provide experimental results from the application of our algorithm on a Minneapolis St. Paul (Twin Cities) traffic dataset to show its effectiveness and usefulness.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 08, 2001
Accession Number
AD1020008

Entities

People

  • Chang-tien Lu
  • Pusheng Zhang
  • Shashi Shekhar

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Computational Complexity
  • Computations
  • Computer Science
  • Cost Models
  • Data Mining
  • Data Sets
  • Databases
  • Detection
  • Detectors
  • Identification
  • Information Science
  • Normal Distribution
  • Probability
  • Statistical Distributions

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Riverine Ecology