Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Database reverse engineering: from the relational to the binary relationship model
Data & Knowledge Engineering
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The Unified Modeling Language user guide
The Unified Modeling Language user guide
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining multiple-level spatial association rules for objects with a broad boundary
Data & Knowledge Engineering
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
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
Reverse Engineering and Design Recovery: A Taxonomy
IEEE Software
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining frequent geographic patterns with knowledge constraints
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Mining and filtering multi-level spatial association rules with ARES
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
A conceptual data model for trajectory data mining
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
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Many association rule-mining algorithms have been proposed in the last few years. Their main drawback is the huge amount of generated patterns. In spatial association rule mining, besides the large amount of rules, many are well-known geographic domain associations explicitly represented in geographic database schemas. Existing algorithms have only considered the data, while the schema has not been considered. The result is that also the associations explicitly represented in geographic database schemas are extracted by association rule-mining algorithms. With the aim to reduce the number of well-known patterns and association rules, this paper presents a summary of results of a novel approach to extract patterns from geographic databases. A two step-pruning method is presented to avoid the generation of association rules that are previously known to be uninteresting. Experiments with real geographic databases show a considerable time reduction in both geographic data pre-processing and spatial association rule mining, with a very significant reduction in the total number of rules.