Difference between spatial and Temporal data mining.

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 Spatial Data Mining

  • Spatial data mining refers to the extraction of knowledge, spatial relationships and interesting patterns that are not specifically stored in a spatial database.
  • It needs space.
  • Primarily, it deals with spatial data such as location, geo-referenced.
  • It involves characteristic rules, discriminant rules, evaluation rules, and association rules.
  • Examples: Finding hotspots, unusual locations.


Temporal Data Mining

  • temporal data mining refers to the process of extraction of knowledge about the occurrence of an event whether they follow, random, cyclic, seasonal variation, etc
  • It needs time.
  • Primarily, it deals with implicit and explicit temporal content, form a huge set of data.
  • It targets mining new patterns and unknown knowledge, which takes the temporal aspects of data.
  • Examples: An association rules which seems - "Any person who buys motorcycle also buys helmet". By temporal aspect, this rule would be - "Any person who buys a motorcycle also buy a helmet after that."


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