[AM10-2] Data mining approaches

Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.

Introduction

External resources

• Miller, H. J. and Han, J. 2001. Geographic data mining and knowledge discovery. New York: Taylor and Francis.
  ISBN number: 9781420073980
• Yuan, M., Buttenfield, B., Gahegan, M., and Miller, H. 2004. Geospatial data mining and knowledge discovery. In R. B. McMaster and E. L. Usery Eds., A Research Agenda for Geographic Information Science, Boca Raton, Florida: CRC Press, 365-388.
  ISBN/section: 9781420038330/chapter14

Learning outcomes

• 6 - Describe how data mining can be used for geospatial intelligence
  Describe how data mining can be used for geospatial intelligence
• 7 - Differentiate between data mining approaches used for spatial and non-spatial applications
  Differentiate between data mining approaches used for spatial and non-spatial applications
• 8 - Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction
  Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction
• 9 - Explain how spatial statistics techniques are used in spatial data mining
  Explain how spatial statistics techniques are used in spatial data mining
• 10 - Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
  Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
• 11 - Demonstrate how cluster analysis can be used as a data mining tool
  Demonstrate how cluster analysis can be used as a data mining tool
• 12 - Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion
  Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion

Self assessment

Outgoing relations

• [AM10-2] Data mining approaches subclassof [AM10] Data mining

Contributors

• Rob Lemmens
  University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
  https://research.utwente.nl/en/persons/rob-lemmens