Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Introduction to logical information systems
Information Processing and Management: an International Journal
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
An algorithm to find frequent concepts of a formal context with taxonomy
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Formal concept analysis enhances fault localization in software
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Contingency structures and concept analysis
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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Formal Concept Analysis (FCA) is a natural framework for learning from positive and negative examples. Indeed, learning from examples results in sets of frequent concepts whose extent contains only these examples. In terms of association rules, the above learning strategy can be seen as searching the premises of exact rules where the consequence is fixed. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are ordered, Conceptual Scaling allows the related taxonomy to be taken into account by producing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant information. In this article, we propose a parameterized generalization of a previously proposed algorithm, in order to learn rules in the presence of a taxonomy. The taxonomy is taken into account during the computation so as to remove all redundancies from intents. Simply changing one component, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm for learning positive and negative rules.