A progressive refinement approach to spatial data mining

The goal of this thesis is to analyze methods for mining of spatial data, and to determine environments in which efficient spatial data mining methods can be implemented. In the spatial data mining process, we use (1) non-spatial properties of the spatial objects and (2) attributes, predicates and functions describing spatial relations between described objects and other features located in the spatial proximity of the described objects. The descriptions are generalized, transformed into predicates, and the discovered knowledge is presented using multiple levels of concepts. We introduce the concept of spatial association rules and present efficient algorithms for mining spatial associations and for the classification of objects stored in geographic information databases. A spatial association rule describes the implication of one or a set of features (or predicates) by another set of features in spatial databases. A spatial classification process is a process that assigns a set of spatial objects into a number of given classes based on a set of spatial and non-spatial features (predicates). The developed algorithms are based on the progressive refinement approach. This approach allows for efficient discovery of knowledge in large spatial databases. A complete set of spatial association rules can be discovered, and accurate decision trees can be constructed, using the progressive refinement approach. Theoretical analysis and experimental results demonstrate the efficiency of the algorithms. The completeness of the set of discovered spatial association rules is shown through the theoretical analysis and the experiments show that the proposed spatial classification algorithm allows for better accuracy of classification than the algorithm proposed in the previous work [37]. The results of the research have been incorporated into the spatial data mining system prototype, GeoMiner. GeoMiner includes five spatial data mining modules: characterizer, comparator, associator, cluster analyzer, and classifier. The SAND (Spatial And Nonspatial Data) architecture has been applied in the modeling of spatial databases. The GeoMiner system includes the spatial data cube construction module, the spatial on-line analytical processing (OLAP) module, and spatial data mining modules. A spatial data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL, for spatial data mining. Moreover, an interactive, user-friendly data mining interface has been constructed and tools have been implemented for visualization of discovered spatial knowledge. (Abstract shortened by UMI.)

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