Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Relative risk and odds ratio: a data mining perspective
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
BLOSOM: a framework for mining arbitrary boolean expressions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Prince: an algorithm for generating rule bases without closure computations
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining succinct systems of minimal generators of formal concepts
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Uncovering and reducing hidden combinatorics in guigues-duquenne bases
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Interactive association rules discovery
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
About the lossless reduction of the minimal generator family of a context
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
A new approach for association rule mining and bi-clustering using formal concept analysis
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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Minimal generators (MGs) are the minimal ones (w.r.t. the number of items) among equivalent itemsets sharing a common set of objects, while their associated closed itemset (CI) is the largest one. The pairs - composed by MGs and their associated CI - divide the itemset lattice into distinct equivalence classes. Such pairs were at the origin of various works related to generic association rule bases, concise representations of frequent itemsets, arbitrary Boolean expressions, etc. Furthermore, the MG set presents some important properties like the order ideal. The latter helped some level-wise bottom-up and even slightly modified depth-first algorithms to efficiently extract interesting knowledge. Nevertheless, the inherent absence of a unique MG associated to a given CI motivates an in-depth study of the possibility of discovering a kind of redundancy within the MG set. This study was started by Dong et al. who introduced the succinct system of minimal generators (SSMG) as an attempt to eliminate the redundancy within this set. In this paper, we give a thorough study of the SSMG as formerly defined by Dong et al. Then, we show that the latter suffers from some drawbacks. After that, we introduce new definitions allowing to overcome the limitations of their work. Finally, an experimental evaluation shows that the SSMG makes it possible to eliminate without information loss an important number of redundant MGs.