Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The Representative Basis for Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
ACM Computing Surveys (CSUR)
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Constructing Iceberg Lattices from Frequent Closures Using Generators
DS '08 Proceedings of the 11th International Conference on Discovery Science
Role Assertion Analysis: a proposed method for ontology refinement through assertion learning
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
A new generic basis of "factual" and "implicative" association rules
Intelligent Data Analysis
Efficient Vertical Mining of Frequent Closures and Generators
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Pattern Structures for Analyzing Complex Data
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Finding minimal rare itemsets and rare association rules
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
An incremental algorithm for mining generators representation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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
Computing Implications with Negation from a Formal Context
Fundamenta Informaticae - Concept Lattices and Their Applications
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Strong association rules are one of basic types of knowledge. The number of rules is often huge, which limits their usefulness. Applying concise rule representations with appropriate inference mechanisms can lessen the problem. Ideally, a rule representation should be lossless (should enable derivation of all strong rules), sound (should forbid derivation of rules that are not strong) and informative (should allow determination of rules' support and confidence). In the paper, we overview the following lossless representations: representative rules, Duquenne-Guigues basis, proper basis, Luxenburger basis, structural basis, minimal non-redundant rules, generic basis, informative basis and its transitive reduction. For each representation, we examine whether it is sound and informative. For the representations that are not sound, we discuss ways of turning them into sound ones. Some important theoretical results related to the relationships among the representations are offered as well.