Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering all most specific sentences
ACM Transactions on Database Systems (TODS)
Mining complex matchings across Web query interfaces
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Database dependency discovery: a machine learning approach
AI Communications
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Towards a Scalable Query Rewriting Algorithm in Presence of Value Constraints
Journal on Data Semantics XII
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Pattern mining problems are useful in many applications. Due to a common theoretical background for such problems, generic concepts can be re-used to easier the development of algorithms. As a consequence, these problems can be implemented with only minimal effort, i.e. programmers do not have to be aware of low-level code, customized data structures and algorithms being available for free. A toolkit, called iZi, has been devised and applied to several problems such as itemset mining, constraint mining in relational databases and query rewriting in data integration systems. According to our first results, the programs obtained using our library offer a very good tradeoff between performances and development simplicity.