Rough set spatial data modeling for data mining

Authors:
Theresa Beaubouef;Roy Ladner;Frederick Petry
Affiliations:
Computer Science Department, Southeastern Louisiana University Hammond, LA 70402;Naval Research Laboratory Mapping, Charting and Geodesy, Stennis Space Center, MS 39529;Naval Research Laboratory Mapping, Charting and Geodesy, Stennis Space Center, MS 39529
Venue:
International Journal of Intelligent Systems - Granular Computing and Data Mining
Year:
2004

Citing 0
Cited 7

Spatial data methods and vague regions: A rough set approach

Applied Soft Computing
Spatial Data Mining with Uncertainty

Computational Intelligence and Security
A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization

IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Rough neural network modeling based on fuzzy rough model and its application to texture classification

Neurocomputing
Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Neural Processing Letters
ROSA: an algebra for rough spatial objects in databases

RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Rough set based approaches to feature selection for Case-Based Reasoning classifiers

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Uncertainty management is necessary for real world applications, especially those used with data mining. The Region Connection Calculus (RCC) and egg-yolk methods have proven useful for the representation of vague regions in spatial data. Rough set theory has been shown to be an effective tool for data mining and for uncertainty management in databases. In this study we use a rough set foundation for expressing topological relationships previously defined for the RCC and egg-yolk methods and show that rough sets can improve on the representation of topological relationships and concepts defined with the other models, which leads to improved mining of spatial data. Finally, we provide an extension of spatial association rule generation that will be able to use rough set–modeled spatial data. © 2004 Wiley Periodicals, Inc.