Condensed Representations of Association Rules in n-ary Relations

Bazin, Alexandre; Gros, Nicolas; Bertaux, Aurélie; Nicolle, Christophe

doi:10.1109/tkde.2022.3153709

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…2): Note that possible implications include those in C ({greek,numbers},{latin}) whose support is in a Cartesian product of dimensions. Such rules (under the more general umbrella of association rules) are already under consideration in the data mining community [14,15]. Additionally, if one is only interested in implications that do not contain the first dimension -the objects -these implications are all derivable from the implications of C ({S1},{S2,...,Sn}) through the application of Armstrong's axioms plus two other axioms, as discussed in [13].…”

Section: The Basics Of Boxesmentioning

confidence: 99%

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Some Notes on Polyadic Concept Analysis

Bazin¹,

Kahn²,

Noûs³

2022

Preprint

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Despite the popularity of Formal Concept Analysis (FCA) as a mathematical framework for data analysis, some of its extensions are still considered arcane. Polyadic Concept Analysis (PCA) is one of the most promising yet understudied of these extensions. This formalism offers many interesting open questions but is hindered in its dissemination by complex notations and a lack of agreed-upon basic definitions. In this paper, we discuss in a mostly informal way the fundamental differences between FCA and PCA in the relation between contexts, conceptual structures, and rules. We identify open questions, present partial results on the maximal size of concept n-lattices and suggest new research directions.

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Section: The Basics Of Boxesmentioning

confidence: 99%

“…As such data are also understudied in data mining in general, this is a unique occasion to position FCA and its extensions as a leading formalism. Some work has already been done in this direction in the field of multidimensional association rule mining [9,10].…”

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confidence: 99%

Some Notes on Polyadic Concept Analysis

Bazin¹,

Kahn²,

Noûs³

2022

Preprint

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“…Next, the weight coefficients determined by association rules are combined to obtain the overall operating status of the transformer. Finally, transformer health status assessment and fault diagnosis are carried out [10]. As shown in Figure 1, the intelligent dispatching control system collects the status information of the transformer, which obtains Fault type according to the alarm information of the equipment, and then we can find the reason according to the Fault type.…”

Section: Introductionmentioning

confidence: 99%

Research on transformer condition evaluation method based on association rule set pair analysis theory

Wang,

Cai,

Chen

et al. 2023

Diagnostyka

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Combining the advantages of set pair analysis and association rules, This paper proposes a transformer condition evaluation based on association rule with set pair analysis theory. In this paper, by analyzing the correlation between the various fault symptoms of transformer, a set of fault types is obtained. At the same time, this paper introduces variable weight formula based on the support degree and confidence degree of association rules, and finally the weight coefficients of fault types and fault symptoms are obtained. By comparing and calculating the support and confidence of association rules, while introducing variable weight formulas, the weight coefficients of fault types and fault symptoms are obtained. it effectively avoid the subjectivity of expert opinions or experiences. Based on the scalability of set pair analysis, a 5-element connection degree is adopted to improve the accuracy of handling uncertain factors in transformer fault diagnosis.

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