Risk discovery of car-related crimes from urban spatial attributes using emerging patterns
International Journal of Knowledge-based and Intelligent Engineering Systems - Chance discovery
A multi-relational approach to spatial classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficiently mining regional outliers in spatial data
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Mining spatial association rules with multi-relational approach
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
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Spatial data mining refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. The approaches usually followed in the analysis of geo-spatial data with the aim of knowledge discovery are essentially characterised by the development of new algorithms, which treat the position and extension of objects mainly through the manipulation of their co-ordinates. In this paper a new approach to this process is presented, where geographic identifiers give the positional aspects of geographic data. These identifiers are manipulated using qualitative reasoning principles, which allow for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The analysis of a demographic database, with the proposed principles, enabled the discovery of patterns that are hidden in the explored geo-spatial and demographic data.