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Proceedings of The Eighteenth International Conference on Fuzzy Set Theory and Applications

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978-80-7599-515-5 FSTA_2026.pdf (5.09 MB)
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2026
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Ostravská univerzita
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A General Framework for Context-Aware Fuzzification of Four Ordered Categories: A Case Study on BMI Categories
(2026) Alijani, Zahra; Daňková, Martina
This paper presents a general methodological framework for constructing context-aware fuzzy partitions that extend conventional crisp categorizations. The approach is based on Nov´ak’s theory of fuzzy contexts and is implemented using the R package lfl. It enables smooth and interpretable transitions between adjacent classes while preserving the original categorical structure. To illustrate the procedure, we apply it to derive fitness-specific fuzzy partitions of Body Mass Index, where the conventional four categories (underweight, normal weight, overweight, obese) are adapted according to individual levels of cardiorespiratory fitness.
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Discovering Fuzzy and Statistical Patterns in Data: The Nuggets R Package
(2026) Burda, Michal
The nuggets package provides a flexible and extensible framework for discovering interpretable data patterns based on frequent logical conditions. Its design unifies classical association – rule mining with linguistic and fuzzy representations, while enabling optional statistical evaluation for selected pattern types such as conditional contrasts and correlations. Pattern generation is driven by support, ensuring efficient mining of relevant conditions, whereas additional quantitative analyses or tests can be seamlessly attached when desired. A major strength of nuggets lies in its extensibility. The framework allows users to define custom fuzzification schemes and to evaluate an arbitrary R function on every frequent condition, thereby enabling the creation of new, user-defined pattern types. This design encourages experimentation with alternative logical semantics, statistical measures, and application – specific evaluation criteria, making nuggets not only a tool for applied pattern discovery but also a research platform for developing new methods.
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Fuzzy-Probabilistic Inference Systems Based on Piecewise Linear Weighted Quantiles
(2026) Cao, Nhung; Holčapek, Michal; Valášek, Radek
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On Data-Driven Fuzzy Partition in the Fuzzy-Probabilistic Inference System Framework
(2026) Cao, Nhung; Holčapek, Michal; Valášek, Radek
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Abstract
Sborník z mezinárodní konference FSTA 2026.
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fuzzy systémy, sborníky konferencí, informatika, matematika
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978-80-7599-514-8
978-80-7599-515-5 (online ; pdf)
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10.15452/978-80-7599-515-5.2026
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https://eduo.osu.cz/handle/1/219
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CC BY 4.0
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Ostravská univerzita
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