Introduction to artificial intelligence and expert systems
Introduction to artificial intelligence and expert systems
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Fast spatial clustering with different metrics and in the presence of obstacles
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
Relational Data Mining
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Using Logical Decision Trees for Clustering
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Relational Distance-Based Clustering
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Graph-based relational learning: current and future directions
ACM SIGKDD Explorations Newsletter
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Flexible matching for noisy structural descriptions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Clustering of German municipalities based on mobility characteristics: an overview of results
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A Complex Networks Approach to Demographic Zonification
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Summarization for geographically distributed data streams
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Global and local spatial autocorrelation in predictive clustering trees
DS'11 Proceedings of the 14th international conference on Discovery science
A density-based spatial clustering for physical constraints
Journal of Intelligent Information Systems
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Clustering is a fundamental task in Spatial Data Mining where data consists of observations for a site (e.g. areal units) descriptive of one or more (spatial) primary units, possibly of different type, collected within the same site boundary. The goal is to group structured objects, i.e. data collected at different sites, such that data inside each cluster models the continuity of socio-economic or geographic environment, while separate clusters model variation over the space. Continuity is evaluated according to the spatial organization arising in data, namely discrete spatial structure, expressing the (spatial) relations between separate sites implicitly defined by their geometrical representation and positioning. Data collected within sites that are (transitively) connected in the discrete spatial structure are clustered together according to the similarity on multi-relational descriptions representing their internal structure. CORSO is a novel spatial data mining method that resorts to a multi-relational approach to learn relational spatial data and exploits the concept of neighborhood to capture relational constraints embedded in the discrete spatial structure. Relational data are expressed in a first-order formalism and similarity among structured objects is computed as degree of matching with respect to a common generalization. The application to real-world spatial data is reported.