Efficient mining of association rules using closed itemset lattices
Information Systems
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A new generic basis of "factual" and "implicative" association rules
Intelligent Data 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
Succinct system of minimal generators: a thorough study, limitations and new definitions
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Efficient generic association rules based classifier approach
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Mining monolingual and bilingual corpora
Intelligent Data Analysis
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Information Sciences: an International Journal
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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The problem of the relevance and the usefulness of extracted association rules is becoming of primary importance, since an overwhelming number of association rules may be derived, even from reasonably sized databases. To overcome such drawback, the extraction of reduced size generic bases of association rules seems to be promising. Using the concept of minimal generator, we propose an algorithm, called Prince, allowing a shrewd extraction of generic bases of rules. To this end, Prince builds the partial order. Its originality is that this partial order is maintained between minimal generators and no more between closed itemsets. A structure called minimal generator lattice is then built, from which the derivation of the generic association rules becomes straightforward. An intensive experimental evaluation, carried out on benchmarking sparse and dense datasets, showed that Prince largely outperforms the pioneer level-wise algorithms, i.e., Close, A-Close and Titanic.