Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Imprecision in Finite Resolution Spatial Data
Geoinformatica
Geoinformatica
Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences: an International Journal
A rough set approach to the discovery of classification rules in spatial data
International Journal of Geographical Information Science
Spatial Database Systems: Design, Implementation and Project Management
Spatial Database Systems: Design, Implementation and Project Management
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Geo-spatial Data Analysis, Quality Assessment and Visualization
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
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Rough set theory has been widely used in spatial analysis. However these applications take little account of the spatial characteristics of spatial data, especially spatial dependencies and correlations. This paper proposes a new method to consider spatially correlated information in rough sets theory. This method divides the attributes of geographical objects into two categories, namely spatial correlated attributes and non-spatial correlated attributes. These two types of attributes are handled separately and the results from both types of attributes are then combined to generate the decision rule. An example is given to illustrate how the new method handles spatially correlated information in rough set theory.