The rough sets theory and evidence theory
Fundamenta Informaticae
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
A new approach to classification based on association rule mining
Decision Support Systems
Approaches to knowledge reduction of covering decision systems based on information theory
Information Sciences: an International Journal
Strategy for mining association rules for web pages based on formal concept analysis
Applied Soft Computing
A novel evolutionary method to search interesting association rules by keywords
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
An efficient rough feature selection algorithm with a multi-granulation view
International Journal of Approximate Reasoning
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In the process of association rule mining on rough set, it is always needed to deleting the reduplicative rows or columns, so supports and confidences of association rules cannot be obtained accurately. While the Hasse diagram of quantitative concept lattice contains all the objects and attributes information, supports of nodes can be obtained visually from the lattice, and the vivid association rule mining can be realized. Association rule mining algorithm on quantitative concept lattice effectively avoids the combinatorial explosion problem existing in rough set. Confidences of rules can be obtained accurately via the supports of relative concept nodes, and it can also effectively avoid the problem of information loss existing in rough set reduction, thus the efficiency of association rule mining can be improved.