Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Modeling KDD Processes within the Inductive Database Framework
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Databases to Mine Queries Faster
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Varro: an algorithm and toolkit for regular structure discovery in treebanks
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Mining formal concepts with a bounded number of exceptions from transactional data
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Remarks on the industrial application of inductive database technologies
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining frequent δ-free patterns in large databases
DS'05 Proceedings of the 8th international conference on Discovery Science
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Data mining in inductive databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Transaction databases, frequent itemsets, and their condensed representations
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
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Frequent set discovery from binary data is an important problem in data mining. It concerns the discovery of a concise representation of large tables from which descriptive rules can be derived, e.g., the popular association rules. Our work concerns the study of two representations, namely frequent sets and frequent closures. N. Pasquier and colleagues designed the close algorithm that provides frequent sets via the discovery of frequent closures. When one mines highly correlated data, apriori-based algorithms clearly fail while close remains tractable. We discuss our implementation of close and the experimental evidence we got from two real-life binary data mining processes. Then, we introduce the concept of almost-closure (generation of every frequent set from frequent almost-closures remains possible but with a bounded error on frequency). To the best of our knowledge, this is a new concept and, here again, we provide some experimental evidence of its add-value.