A database perspective on knowledge discovery
Communications of the ACM
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Theory of Inductive Query Answering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A framework for data mining pattern management
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
An automata approach to pattern collections
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
An efficient framework for mining flexible constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Computation of mining queries: an algebraic approach
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
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Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.