2009 AMultiRelationalApproachtoSpati

• (Frank et al., 2009) ⇒ Richard Frank, Martin Ester, and Arno Knobbe. (2009). “A Multi-relational Approach to Spatial Classification.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557058

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Cited By

• http://scholar.google.com/scholar?q=%22A+multi-relational+approach+to+spatial+classification%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557058&preflayout=flat#citedby

Quotes

Author Keywords

aggregation, parallelization, spatial classification, spatial data mining

Abstract

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 additions to the multi-relational data mining framework is proposed, to be able to represent spatial aggregations as well as spatial features and literals. These additions allow for capturing more complex interactions and spatial occurrences such as spatial trends. In order to more efficiently perform the rule learning and exploit powerful multi-processor machines, a scalable parallelized method capable of reducing the runtime by several factors is presented. The method is compared against existing methods by experimental evaluation on a real world crime dataset which demonstrate the importance of the neighbourhood definition and the advantages of parallelization.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 AMultiRelationalApproachtoSpati	Martin Ester Richard Frank Arno Knobbe			A Multi-relational Approach to Spatial Classification		KDD-2009 Proceedings		10.1145/1557019.1557058		2009