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
Relational Data Mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
Emerging Pattern Based Classification in Relational Data Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Relational Frequent Patterns Mining for Novelty Detection from Data Streams
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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The discovery of emerging patterns (EPs) is a descriptive data mining task defined for pre-classified data. It aims at detecting patterns which contrast two classes and has been extensively investigated for attribute-value representations. In this work we propose a method, named Mr-EP, which discovers EPs from data scattered in multiple tables of a relational database. Generated EPs can capture the differences between objects of two classes which involve properties possibly spanned in separate data tables. We implemented Mr-EP in a pre-existing multi-relational data mining system which is tightly integrated with a relational DBMS, and then we tested it on two sets of geo-referenced data.