Logic programming and databases
Logic programming and databases
Fast computation of generalized Voronoi diagrams using graphics hardware
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
ECML '93 Proceedings of the European Conference on Machine Learning
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
Proceedings of the 2004 ACM symposium on Applied computing
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Spatial associative classification at different levels of granularity: a probabilistic approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Geo-spatial data mining in the analysis of a demographic database
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning to combine discriminative classifiers: confidence based
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Outlier detection in relational data: A case study in geographical information systems
Expert Systems with Applications: An International Journal
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
A method for extracting rules from spatial data based on rough fuzzy sets
Knowledge-Based Systems
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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.