Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of 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
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
Using transposition for pattern discovery from microarray data
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Database transposition for constrained (closed) pattern mining
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A Data Mining Formalization to Improve Hypergraph Minimal Transversal Computation
Fundamenta Informaticae
Supporting bi-cluster interpretation in 0/1 data by means of local patterns
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Efficient mining under rich constraints derived from various datasets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
A Data Mining Formalization to Improve Hypergraph Minimal Transversal Computation
Fundamenta Informaticae
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Mining patterns under constraints in large data (also called fat data) is an important task to benefit from the multiple uses of the patterns embedded in these data sets. It is a difficult task due to the exponential growth of the search space according to the number of attributes. From such contexts, closed patterns can be extracted by using the properties of the Galois connections. But, from the best of our knowledge, there is no approach to extract interesting patterns like δ-free patterns which are on the core of a lot of relevant rules. In this paper, we propose a new method based on an efficient way to compute the extension of a pattern and a pruning criterion to mine frequent δ-free patterns in large databases. We give an algorithm (FTminer) for the practical use of this method. We show the efficiency of this approach by means of experiments on benchmarks and on gene expression data.