Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization techniques for queries with expensive methods
ACM Transactions on Database Systems (TODS)
Rule Discovery in Telecommunication AlarmData
Journal of Network and Systems Management
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Querying Inductive Databases: A Case Study on the MINE RULE Operator
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Towards an Ontology of Data Mining Investigations
DS '09 Proceedings of the 12th International Conference on Discovery Science
The iZi project: easy prototyping of interesting pattern mining algorithms
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Inductive databases and constraint-based data mining
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
Contribution to gene expression data analysis by means of set pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Inductive databases in the relational model: the data as the bridge
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Semantic knowledge integration to support inductive query optimization
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally defined generalizations about the data. We formalize this concept and show how it can be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be defined using normal database terminology. We demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.