Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
Mining multiple-level spatial association rules for objects with a broad boundary
Data & Knowledge Engineering
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
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Customer-oriented catalog segmentation: effective solution approaches
Decision Support Systems
A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining
Expert Systems with Applications: An International Journal
Intelligent profitable customers segmentation system based on business intelligence tools
Expert Systems with Applications: An International Journal
Efficient discovery of multilevel spatial association rules using partitions
Information and Software Technology
Rough classification in incomplete information systems
Mathematical and Computer Modelling: An International Journal
From data to global generalized knowledge
Decision Support Systems
Direct mailing decisions based on the worst and best practice cross-efficiency evaluations
International Journal of Business Information Systems
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
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Spatial attributes are important factors for predicting customer behavior. However, thorough studies on this subject have never been carried out. This paper presents a new idea that incorporates spatial predicates describing the spatial relationships between customer locations and surrounding objects into customer attributes. More specifically, we developed two algorithms in order to achieve spatially enabled customer segmentation. First, a novel filtration algorithm is proposed that can select more relevant predicates from the huge amounts of spatial predicates than existing filtration algorithms. Second, since spatial predicates fundamentally involve some uncertainties, a rough set-based spatial data classification algorithm is developed to handle the uncertainties and therefore provide effective spatial data classification. A series of experiments were conducted and the results indicate that our proposed methods are superior to existing methods for data classification.