Spatial Data Methods and Vague Regions: A Rough Set Approach

Abstract

Uncertainty management has been considered essential for real world applications, and spatial data and geographic information systems in particular require some means for managing uncertainty and vagueness. Rough sets have been shown to be an effective tool for data mining and uncertainty management in databases. The 9-intersection, region connection calculus (RCC), and egg-yolk methods have proven useful for modeling topological relations in spatial data. In this paper, we apply rough set definitions for topological relationships based on the 9-intersection, RCC, and egg-yolk models for objects with broad boundaries. We show that rough sets can be used to express and improve on topological relationships and concepts defined with these models.

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Document Details

Document Type
Technical Report
Publication Date
Jul 09, 2003
Accession Number
ADA458392

Entities

People

  • Frederick E. Petry
  • Roy Ladner
  • Theresa Beaubouef

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Boundaries
  • Clustering
  • Computer Science
  • Data Mining
  • Data Science
  • Databases
  • Fuzzy Sets
  • Geographic Information Systems
  • Geometry
  • Information Retrieval
  • Information Science
  • Information Systems
  • Military Research
  • Relational Databases
  • Set Theory
  • Uncertainty

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • AI & ML