Database reverse engineering: from the relational to the binary relationship model
Data & Knowledge Engineering
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Reverse Engineering and Design Recovery: A Taxonomy
IEEE Software
Fast Algorithms for Mining Association Rules in Large Databases
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th 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
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
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
International Journal of Geographical Information Science
A location based text mining method using ANN for geospatial KDD process
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
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The large amount of patterns generated by frequent pattern mining algorithms has been extensively addressed in the last few years. In geographic pattern mining, besides the large amount of patterns, many are well known geographic domain associations. Existing algorithms do not warrant the elimination of all well known geographic dependences since no prior knowledge is used for this purpose. This paper presents a two step method for mining frequent geographic patterns without associations that are previously known as non-interesting. In the first step the input space is reduced as much as possible. This is as far as we know still the most efficient method to reduce frequent patterns. In the second step, all remaining geographic dependences that can only be eliminated during the frequent set generation are removed in an efficient way. Experiments show an elimination of more than 50% of the total number of frequent patterns, and which are exactly the less interesting.