Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Granular Computing on Binary Relations
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
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Association rules in data mining are considered from a point of view of conditional logic and rough sets. In our previous work, given an association rule in some fixed database, its corresponding Kripke model was formulated. Then, two difficulties in the formulation were pointed out: limitation of the form of association rules and limited formulation of the models themselves. To resolve the defects, Chellas's conditional logic was introduced and thereby, the class of conditionals in conditional logic can be naturally regarded as containing the original association rules. In this paper, further, an extension of conditional logic is introduced for dealing with association rules with intermediate values of confidence based on the idea of fuzzy-measure-based graded modal logic.