A systematic approach to the assessment of fuzzy association rules

Dubois, Didier; Hüllermeier, Eyke; Prade, Henri

doi:10.1007/s10618-005-0032-4

Cited by 160 publications

(83 citation statements)

References 71 publications

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“…Introducing fuzzy implicative rules in modelling accounts for constraints or landmark points the model should comply with (as opposed to observed data) [93]. The bipolar view of rules in terms of examples and counterexamples may turn out to be very useful when extracting fuzzy rules from data [57].…”

Section: Basic Notions Of Possibility Theorymentioning

confidence: 99%

Possibility Theory and Its Applications: Where Do We Stand?

Dubois

Prade

2015

Springer Handbook of Computational Intelligence

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141

101

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This paper provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability and non-monotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and common-sense reasoning with exception-tainted knowledge in Artificial Intelligence. Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. Qualitative possibility theory has been axiomatically justified in a decision-theoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statistical reasoning with imprecise probabilities. As such it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information.

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Section: Basic Notions Of Possibility Theorymentioning

confidence: 99%

Possibility Theory and Its Applications: Where Do We Stand?

Dubois

Prade

2015

Springer Handbook of Computational Intelligence

Self Cite

141

101

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“…Comparing this approach with the classical setting of association analysis, CT plays the role of the support of a rule, while Fuzzy γ corresponds to the confidence. These measures can also be interpreted within the formal framework proposed by Dubois and Hullermeier in [7], in which every observation (in the case of a pair of points (A(u), B(u)) and (A(v), B(v)) is considered, to a certain degree, as an example of a pattern, as a counterexample, or as being irrelevant for the evaluation of the pattern. In the framework and the algorithm of Koh and Hullermeier, these degrees are given, respectively, by the degree of concordance, the degree of discordance, and the degree to which the pair is a tie.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Orderings for Fuzzy Gradual Patterns

Quintero

Laurent

Poncelet

2011

Flexible Query Answering Systems

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Abstract. In mining gradual patterns the idea is to express co-variations of attributes, taking the direction of change of attribute values into account. These patterns are such as {the more A, the more B}, {the more A, the more B, the less C} or { the higher the speed, the higher the danger }. These patterns are denoted as {A≥B≥ }, { A≥B≥C≤} or { speed≥danger≥} respectively. Such patterns hold if the variation constraints simultaneously hold on the attributes. However, it is often hardly possible to compare attribute values, either because the values are taken from noisy data, or because it is difficult to consider that a small difference between two values is meaningful. In this context, we focus on the use of fuzzy orderings to take this into account. abstract environment.

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“…It emphasizes the advantages of using conjointly implicative rules (encoding negative information) and conjunctive rules (encoding positive information) in the same rule-based system. Finally the bipolar view is instrumental in rigourously extending the support and the confidence degrees to fuzzy association rules [9].…”

Section: Bipolarity and If-then Rulesmentioning

confidence: 99%