Computing the minimum cover of functional dependencies
Information Processing Letters
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
New results on monotone dualization and generating hypergraph transversals
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hypergraph Transversal Computation and Related Problems in Logic and AI
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
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
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
A statistical framework for mining substitution rules
Knowledge and Information Systems
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Theory of Relational Databases
Theory of Relational Databases
A global parallel algorithm for the hypergraph transversal problem
Information Processing Letters
Mining Negative and Positive Influence Rules Using Kullback-Leibler Divergence
ICCGI '07 Proceedings of the International Multi-Conference on Computing in the Global Information Technology
Constructing Iceberg Lattices from Frequent Closures Using Generators
DS '08 Proceedings of the 11th International Conference on Discovery Science
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Mining a complete set of both positive and negative association rules from large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Generating positive and negative exact rules using formal concept analysis: problems and solutions
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Galois lattices and bases for MGK-valid association rules
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Extraction of association rules based on literalsets
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The objective of this article is to define an approach towards generating implications with (or without) negation when only a formal context K = (G, M, I) is provided. To that end, we define a two-step procedure which first (i) computes implications whose premise is a key in the context K | $\tilde{\rm K}$ representing the apposition of the context K and its complementary $\tilde{\rm K}$ with attributes in $\tilde{\rm M}$ (negative attributes), and then (ii) uses an inference axiom we have defined to produce the whole set of implications.