The magic of duplicates and aggregates
Proceedings of the sixteenth international conference on Very large databases
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Intensional query processing using data mining approaches
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using domain knowledge in knowledge discovery
Proceedings of the eighth international conference on Information and knowledge management
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Some Results on Flexible-Pattern Discovery
COM '00 Proceedings of the 11th Annual Symposium on Combinatorial Pattern Matching
Data mining: a new arsenal for strategic decision-making
Data warehousing and web engineering
Finding unexpected patterns in data
Data mining, rough sets and granular computing
Direct Interesting Rule Generation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Sequential pattern mining algorithm for automotive warranty data
Computers and Industrial Engineering
MASSON: discovering commonalities in collection of objects using genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Connection network and optimization of interest metric for one-to-one marketing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Expert Systems with Applications: An International Journal
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Using bees to solve a data-mining problem expressed as a max-sat one
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Interestingness measures for fixed consequent rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user.