Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and 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
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Emulating a Cooperative Behavior in a Generic Association Rule Visualization Tool
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
M-CLANN: Multi-class Concept Lattice-Based Artificial Neural Network for Supervised Classification
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A Framework for Problem-Solving Knowledge Mining from Users' Actions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
A new generic basis of "factual" and "implicative" association rules
Intelligent Data 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
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Mining top-K non-redundant association rules
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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The problem of the relevance and the usefulness of extracted association rules is becoming paramount, since an overwhelming number of association rules may be derived from even reasonably sized real-life databases. A possible solution consists in using results of Formal Concept Analysis to generate a generic base of association rules. This set, of reduced size, makes it possible to derive all the association rules via an adequate axiomatic system. In this paper, we introduce a novel generic and informative base of association rules, conveying two types of knowledge: “factual” and “implicative”. We present also a valid and complete axiomatic system allowing to derive the set of all association rules. Results of the experiments carried out on real-life databases showed important profits in terms of compactness of the introduced generic base.