Spatial reasoning based spatial data mining for precision agriculture

  • Authors:

  • Sheng-sheng Wang;Da-you Liu;Xin-ying Wang;Jie Liu

  • Affiliations:

  • Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry, of Education, College of Computer Science and Technology, Institute of Mathematics, Jilin University, Changchun, China;Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry, of Education, College of Computer Science and Technology, Institute of Mathematics, Jilin University, Changchun, China;Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry, of Education, College of Computer Science and Technology, Institute of Mathematics, Jilin University, Changchun, China;Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry, of Education, College of Computer Science and Technology, Institute of Mathematics, Jilin University, Changchun, China

  • Venue:
  • APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
  • Year:
  • 2006

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Abstract

Knowledge discovery in spatial databases represents a particular case of discovery, allowing the discovery of relationships that exist between spatial and non-spatial data. Spatial reasoning ought to play a very important role in spatial data mining, but the research combined SR and SDM are very few. This paper describes the conception and implementation of SRSDM, the tool for data mining in spatial databases based on spatial reasoning method. Most spatial data mining systems only support topological relation, nearly all previous GIS and AI researches focused on single spatial aspect . Those were quite inadequate for practical applications. We propose a new spatial knowledge representation which integrates topology, direction, distance and size relations. SRSDM includes three parts: extracting spatial relations, frameworks for traditional or new data mining algorithms.