Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Designing Templates for Mining Association Rules
Journal of Intelligent Information Systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational problems related to the design of normal form relational schemas
ACM Transactions on Database Systems (TODS)
Minimum Covers in Relational Database Model
Journal of the ACM (JACM)
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Enhancing quality of knowledge synthesized from multi-database mining
Pattern Recognition Letters
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A Galois Lattice framework to handle updates in the mining of closed itemsets in dynamic databases
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Discovering shared conceptualizations in folksonomies
Web Semantics: Science, Services and Agents on the World Wide Web
Knowledge Acquisition from a Medical Corpus: Use and Return on Experiences
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Minimum-Size Bases of Association Rules
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Deriving non-redundant approximate association rules from hierarchical datasets
Proceedings of the 17th ACM conference on Information and knowledge management
Deduction Schemes for Association Rules
DS '08 Proceedings of the 11th International Conference on Discovery Science
Utilizing Non-redundant Association Rules from Multi-level Datasets
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Mining Association Rule Bases from Integrated Genomic Data and Annotations
Computational Intelligence Methods for Bioinformatics and Biostatistics
Data & Knowledge Engineering
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
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
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Two measures of objective novelty in association rule mining
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Incremental construction of alpha lattices and association rules
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Mining monolingual and bilingual corpora
Intelligent Data Analysis
Knowledge extraction using a conceptual information system (ExCIS)
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
DS'10 Proceedings of the 13th international conference on Discovery science
Valuations and closure operators on finite lattices
Discrete Applied Mathematics
Reliable representations for association rules
Data & Knowledge Engineering
The agree concept lattice for multidimensional database analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Interesting patterns extraction using prior knowledge
DS'06 Proceedings of the 9th international conference on Discovery Science
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
Using association rules to solve the cold-start problem in recommender systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Structures of association rule set
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
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
Mining frequent itemsets with dualistic constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Tractable reasoning problems with fully-characterized association rules
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Pattern-based solution risk model for strategic IT outsourcing
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Association rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules. Moreover, many of these rules are redundant and thus useless. Using a semantic based on the closure of the Galois connection, we define a condensed representation for association rules. This representation is characterized by frequent closed itemsets and their generators. It contains the non-redundant association rules having minimal antecedent and maximal consequent, called min-max association rules. We think that these rules are the most relevant since they are the most general non-redundant association rules. Furthermore, this representation is a basis, i.e., a generating set for all association rules, their supports and their confidences, and all of them can be retrieved needless accessing the data. We introduce algorithms for extracting this basis and for reconstructing all association rules. Results of experiments carried out on real datasets show the usefulness of this approach. In order to generate this basis when an algorithm for extracting frequent itemsets--such as Apriori for instance--is used, we also present an algorithm for deriving frequent closed itemsets and their generators from frequent itemsets without using the dataset.