A multi-relational approach to spatial classification

Spatial classification is the task of learning models to predict class labels based on the features of entities as well as the spatial relationships to other entities and their features. Spatial data can be represented as multi-relational data, however it presents novel challenges not present in multi-relational problems. One such problem is that spatial relationships are embedded in space, unknown a priori, and it is part of the algorithm's task to determine which relationships are important and what properties to consider. In order to determine when two entities are spatially related in an adaptive and non-parametric way, we propose a Voronoi-based neighbourhood definition upon which spatial literals can be built. Properties of these neighbourhoods also need to be described and used for classification purposes. Non-spatial aggregation literals already exist within the multi-relational framework, but are not sufficient for comprehensive spatial classification. A formal set of ad...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Richard Frank, Martin Ester, Arno Knobbe
• Keywords: Comprehensive Spatial Classification, Data Mining, KDD 2009, Spatial Data Mining, Spatial Literals