Spatial data mining, cluster and pattern recognition

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Abstract

Extracting meaningful patterns from large databases is a relevant task in several areas of geographic research such as the interpretation of satellite images, the study of dispersion of spatial phenomena (e.g., diseases, crime), and classification of space-time behavior of individuals, to name a few. This article discusses the techniques and issues involved in spatial data mining for cluster detection and pattern recognition. The techniques range from inductive machine learning algorithms to numerical cluster detection techniques. Irrespective of the technique used, a number of issues require attention in any spatial data-mining task. These include validity testing, the selection of relevant features, interpretation of patterns, and treatment of spatial data. Approaches developed to address these issues are discussed in this article as well.

Original languageEnglish
Title of host publicationInternational Encyclopedia of Human Geography
PublisherElsevier
Pages325-331
Number of pages7
ISBN (Electronic)9780080449104
ISBN (Print)9780080449111
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • Classification
  • Cluster analysis
  • Geographic analysis
  • Knowledge discovery
  • Machine learning
  • Pattern recognition
  • Spatial data mining
  • Spatial databases
  • Supervised learning
  • Unsupervised learning

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