A database perspective on knowledge discovery
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
A Comparison between Query Languages for the Extraction of Association Rules
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
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
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational computation for mining association rules from XML data
Proceedings of the 14th ACM international conference on Information and knowledge management
A Logic-Based Approach to Mining Inductive Databases
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Small subset queries and bloom filters using ternary associative memories, with applications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Logic-Based association rule mining in XML documents
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
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Storing sets and querying them (e.g., subset queries that provide all supersets of a given set) is known to be difficult within relational databases. We consider that being able to query efficiently both transactional data and materialized collections of sets by means of standard query language is an important step towards practical inductive databases. Indeed, data mining query languages like MINE RULE extract collections of association rules whose components are sets into relational tables. Post-processing phases often use extensively subset queries and cannot be efficiently processed by SQL servers. In this paper, we propose a new way to handle sets from relational databases. It is based on a data structure that partially encodes the inclusion relationship between sets. It is an extension of the hash group bitmap key proposed by Morzy et al. [8]. Our experiments show an interesting improvement for these useful subset queries.