A Fuzzy Close Algorithm for Mining Fuzzy Association Rules

Pierrard, Régis; Poli, Jean-Philippe; Hudelot, Céline

doi:10.1007/978-3-319-91476-3_8

Cited by 9 publications

(9 citation statements)

References 12 publications

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“…For one given class, instances should share the relations that are relevant, which means that they are highly correlated to each other. In order to take advantage of this property, we decided to rely on a fuzzy frequent itemset mining called Fuzzy Close [67]. This algorithm relies on a closure operator to take advantage of the high correlation between instances.…”

Section: Learning Relevant Relations and Descriptorsmentioning

confidence: 99%

Spatial relation learning for explainable image classification and annotation in critical applications

Pierrard

Poli

Hudelot

2021

Artificial Intelligence

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Section: Learning Relevant Relations and Descriptorsmentioning

confidence: 99%

Spatial relation learning for explainable image classification and annotation in critical applications

Pierrard

Poli

Hudelot

2021

Artificial Intelligence

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“…There are also studies that generalize the Apriori algorithm to fuzzy data which have preserved the algorithm's traditional abstraction. These methods face increasing computational complexity [34][21] [28]. 6.…”

Section: Related Workmentioning

confidence: 99%

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Boudaghi

Mahan

Isazadeh

2021

Preprint

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It is crystal clear that discovering the rules for finding a specific pattern among given data for extraction of association rules and rule-based learning systems has been defined. There is much research that make use of game theory for the processes contributing to discovery of rules. In recent years, modeling based on game theory in rule learning sphere has gained much more attention by computer scientists. When there is independence between the strategies used by two or more different players the strategy game modeling could be used. In this view, strategic play is a desirable model for situations with no permanent strategic relationship among interactions. In addition, Nash equilibrium is the most widely used solution concept in game theory. This concept is a state-of-the-art interpretation of a strategy game. Each player has an accurate prediction of other players' behavior and acts according to such a rational prediction. In the present study, by extracting rules from frequent patterns we have presented a model that can extract learning rules by abstraction based on game theory, which can be used not only for association rules but also for rule-based learning systems. Also, the introduced method can be easily generalized to fuzzy data. To Find Nash Equilibrium (FNE) in the proposed method, we used meta-heuristic Bus Transportation Algorithm (BTA).The results indicated that the method reduces computational complexity in the associate rule discovery process and rule learning, provided that FNE is solved.

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“…The frequent itemset mining algorithm [20] we use goes through two phases. First, it looks for every frequent closed set of attributes.…”

Section: Extracting Frequent Fuzzy Relations and Propertiesmentioning

confidence: 99%

Learning Fuzzy Relations and Properties for Explainable Artificial Intelligence

Pierrard

Poli

Hudelot

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The goal of explainable artificial intelligence is to solve problems in a way that humans can understand how it does it. However, few approaches have been proposed so far and some of them lay more emphasis on interpretability than on explainability. In this paper, we propose an approach that is based on learning fuzzy relations and fuzzy properties. We extract frequent relations from a dataset to generate an explained decision. Our approach can deal with different problems, such as classification or annotation. A model was built to perform explained classification on a toy dataset that we generated. It managed to correctly classify examples while providing convincing explanations. A few areas for improvement have been spotted, such as the need to filter relations and properties before or while learning them in order to avoid useless computations.

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A Fuzzy Close Algorithm for Mining Fuzzy Association Rules

Cited by 9 publications

References 12 publications

Spatial relation learning for explainable image classification and annotation in critical applications

Spatial relation learning for explainable image classification and annotation in critical applications

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Learning Fuzzy Relations and Properties for Explainable Artificial Intelligence

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