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Advances in knowledge discovery and data mining
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Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
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IEEE Transactions on Knowledge and Data Engineering
Generating Logic Descriptions for the Automated Interpretation of Topographic Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
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Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Analyzing multi-level spatial association rules through a graph-based visualization
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
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GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Relational Data Mining Applied to Virtual Engineering of Product Designs
Inductive Logic Programming
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PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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Classification of symbolic objects: A lazy learning approach
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Temporal-spatial association analysis of ocean salinity and temperature variations
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Mining spatial association rules with multi-relational approach
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Expert Systems with Applications: An International Journal
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
Mining and filtering multi-level spatial association rules with ARES
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
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Census data mining has great potential both in business development and in good public policy, but still must be solved in this field a number of research issues. In this paper, problems related to the geo-referenciation of census data are considered. In particular, the accommodation of the spatial dimension in census data mining is investigated for the task of discovering spatial association rules, that is, association rules involving spatial relations among (spatial) objects. The formulation of a new method based on a multi-relational data mining approach is proposed. It takes advantage of the representation and inference techniques developed in the field of Inductive Logic Programming (ILP). In particular, the expressive power of predicate logic is profitably used to represent both spatial relations and background knowledge, such as spatial hierarchies and rules for spatial qualitative reasoning. The logical notions of generality order and of the downward refinement operator on the space of patterns are profitably used to define both the search space and the search strategy. The proposed method has been implemented in the ILP system SPADA (Spatial Pattern Discovery Algorithm). SPADA has been interfaced both to a module for the extraction of spatial features from a spatial database and to a module for numerical attribute discretization. The three modules have been used in an application to urban accessibility of a hospital in Stockport, Greater Manchester. Results obtained through a spatial analysis of geo-referenced census data are illustrated.