SPARC: Spatial Association Rule-Based Classification

Abstract

Spatial classification is to classify spatial objects based on the spatial and nonspatial features of these objects in a database. The classification results, taken as the models for the data, can be used for better understanding of the relationships among the objects in the database and for prediction of characteristics and features of new objects. Spatial classification is a challenging task due to the sparsity of spatial features which leads to high dimensionality and also the “curse of dimensionality. In this paper, we introduce an association-based spatial classification algorithm, called SPARC (SPatial Association Rule-based Classification), for efficient spatial classification in large geospatial databases. SPARC explores spatial association-based classification and integrates a few important techniques developed in spatial indexing and data mining to achieve high scalability when classifying a large number of spatial data objects. These techniques include micro-clustering, spatial join indexing, feature reduction by frequent pattern mining, and association-based classification. Our performance study shows that SPARC is efficient for classification of spatial objects in large databases.