IterativeSOMSO: An iterative self-organizing map for spatial outlier detection

Qiao Cai, Haibo He, Hong Man, Jianlong Qiu

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

10 Scopus citations

Abstract

In this paper, we propose an iterative self-organizing map approach for spatial outlier detection (IterativeSOMSO). IterativeSOMSO method can address high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features. Detection of spatial outliers facilitates further discovery of spatial distribution and attribute information for data mining problems. The experimental results indicate our proposed approach can be effectively implemented for the large spatial dataset based on U.S. Census Bureau with approving performance.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings
Pages325-330
Number of pages6
EditionPART 1
DOIs
StatePublished - 2010
Event7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China
Duration: 6 Jun 20109 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6063 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Symposium on Neural Networks, ISNN 2010
Country/TerritoryChina
CityShanghai
Period6/06/109/06/10

Keywords

  • Mahalanobis distance
  • Neural network
  • Self-organizing map
  • Spatial data mining
  • Spatial outlier

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