Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Controlling patterns of geospatial phenomena
Geoinformatica
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A clustering-based visualization of colocation patterns
Proceedings of the 15th Symposium on International Database Engineering & Applications
Discovering evolution chains in dynamic networks
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
| Hi-index | 0.00 |
Spatial Data Mining (SDM) has great potential in supporting public policy and in underpinning society functioning. One task in SDM is the discovery of characterization and peculiarities of communities sharing socio-economic aspects in order to identify potentialities, needs and public intervention. Emerging patterns (EPs) are a special kind of pattern which contrast two classes. In this paper, we face the problem of extracting EPs from spatial data. At this aim, we resort to a multi-relational approach in order to deal with the degree of complexity of discovering EPs from spatial data (i.e., (i) the spatial dimension implicitly defines spatial properties and relations, (ii) spatial phenomena are affected by autocorrelation). Experiments on real datasets are described.