ABSTRACT Destination Management Organisations (DMOs) are driving the sustainability transformation at territorial level. The novel adoption of Fuzzy Formal Concept Analysis to analyse a census survey (n = 109) of Italian DMOs highlights the potential of FFCA to uncover nuanced patterns of sustainability implementation that remain hidden using more conventional methods: the co-existence of operational tourism flows management and strategic efforts aimed at advancing sustainability. This represents not only a methodological innovation and a valuable path to process categorical data from survey designs, but also a novel practice-oriented insight for destination managers aiming at implementing sustainability practices.

In Fuzzy Formal Concept Analysis (FFCA), concept lattices are computed by scaling the problem and applying ordinary FCA algorithms. In this paper, the CbO family of algorithms is extended to work natively in the fuzzy setting, they are proved to be correct and output the whole set of formal concepts, which makes them mathematically equivalent to the scaling approach. However, experimental results demonstrate the performance improvement of these methods compared to scaling. The paper also discusses a new fuzzy strategy based on blacklisting redundant truth values to enhance the performance of algorithms by taking advantage of the structure of the residuated lattice.

TopicFFCA: A short-text topic-detection approach by fuzzy formal concept analysis

This study introduces a cognition-enhanced framework for geospatial decision-making by integrating Fuzzy Formal Concept Analysis (FCA), the Surprisingly Popular (SP) method, and a Large Language Model (GPT-4o). Our approach captures cognitive influences that are often overlooked in traditional geospatial analyses. Fuzzy FCA is used to extract interpretable concept hierarchies from spatial data, while the GPT-4o model estimates SP scores, identifying choices that reflect underlying cognitive biases. These cognitively informed features are combined within machine learning models, improving both prediction accuracy and interpretability. Experiments on real-world urban mobility and environmental risk scenarios demonstrate significant performance gains, with models like XGBoost achieving an accuracy of 0.8412. We also introduce a novel method for evaluating the cognitive validity of LLM-generated model explanations, which involves assessing how well these explanations align with human intuition and reasoning. Our results show that incorporating cognitive elements into geospatial models not only improves outcomes but also bridges the gap between data-driven predictions and human decision-making. This framework offers broad potential for applications in GIS, urban planning, and environmental management.

The process of decomposing databases into smaller datasets, with the objective of extrapolating the information obtained in the smaller ones to the original database, represents a relevant and complex challenge in real applications. It is particularly relevant in the context of fuzzy formal concept analysis, where the complexities of knowledge extraction from datasets characterized by incomplete and imperfect data are considerable. This paper will analyze a mechanism and different properties for detecting independent subcontexts from a given context, using modal operators within the multi-adjoint concept lattice framework.

Roughness in formal concept analysis via multilattices

Fuzzy Formal Concept Analysis (FFCA) is a robust mathematical tool for analyzing data, particularly where uncertainty or fuzziness is inherent. FFCA is utilized across various domains, including data mining, information retrieval, and knowledge representation. However, fuzzy concepts extraction is a crucial yet computationally intensive task. This paper addresses the challenge of time efficiency in extracting single-sided fuzzy concepts from large datasets. A parallel algorithm is proposed to reduce computational time and optimize resource utilization, thus enabling the scalable analysis of expanding datasets. By computing fuzzy concepts across multiple threads in parallel, each thread processes an attribute independently to extract fuzzy concepts, which are then merged in the final step. The proposed algorithm extracts fuzzy-crisp concepts, which are more concise than other types of fuzzy concepts. Experiments were conducted to evaluate the performance of the proposed parallel algorithm against existing sequential methods. Experimental results demonstrate significant gains in computational efficiency, with the algorithm achieving an average time reduction of 68% compared to the attribute-based algorithm and up to 83%-time reduction compared to the fuzzy CbO algorithm across various types of datasets, including binary, quantitative, and fuzzy.

Dynamic updating variable precision three-way concept method based on two-way concept-cognitive learning in fuzzy formal contexts

Concept-drift detection index based on fuzzy formal concept analysis for fake news classifiers

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fuzzy relational formal concept analysis (FRCA)</i> mines collections of fuzzy concept lattices from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy relational context families</i> , which are special datasets made of fuzzy formal contexts and fuzzy relations between objects of different types. Mainly, FRCA consists of the following procedures: first, an initial fuzzy relational context family is transformed into a collection of fuzzy formal contexts; second, a fuzzy concept lattice is generated from each fuzzy formal context by using one of the techniques existing in the literature. The principal tools to transform a fuzzy context family into a set of fuzzy formal contexts are the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy scaling quantifiers</i> , which are particular fuzzy quantifiers based on the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evaluative linguistic expression</i> . FRCA can be applied whenever information needs to be extracted from multirelational datasets including vagueness, and it can be viewed as an extension of both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relational concept analysis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy formal concept analysis</i> . This article contributes to the development of fuzzy relational concept analysis by achieving the following goals. First of all, we present and study a new class of fuzzy quantifiers, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-scaling quantifiers</i> , to extract fuzzy concepts from fuzzy relational context families. Subsequently, we provide an algorithm to generate, given a t-scaling quantifier, a collection of fuzzy concept lattices from a special fuzzy relational context family, which is composed of a pair of fuzzy formal contexts and a fuzzy relation between their objects. After that, we introduce an ordered relation on the set of all t-scaling quantifiers, which allows us to discover a correspondence among fuzzy concept lattices deriving from different t-scaling quantifiers. Finally, we discuss how the results obtained for t-scaling quantifiers can be extended to the class of fuzzy scaling quantifies. Therefore, this analysis highlights the main differences between t-scaling and fuzzy quantifiers.

Incremental concept-cognitive learning approach for concept classification oriented to weighted fuzzy concepts

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

Graded cubes of opposition in fuzzy formal concept analysis

Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic

PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

Graded polygons of opposition in fuzzy formal concept analysis

Measuring semantic similarity/semantic relatedness is an important task in computational linguistic, natural language processing and ontology creation. In this paper, a new hybrid method using LSA and FFCA is proposed for computing the semantic-relatedness. Latent semantic analysis (LSA) is used to extract the attributes of the concepts and these attributes are further mapped to FFCA to compute semantic relatedness. The latent semantic analysis is used for finding the neighbouring words or attributes and their correlation value. The concepts and their attributes are mapped to FCA table and then to FFCA table by using the correlation value as membership. A fuzzy similarity measure is then used to compute the semantic relatedness between these concepts/words. The proposed method is evaluated on word similarity bench mark dataset WS-353 and found an accuracy of 0.85.

Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis

The theory of formal concept analysis(FCA), which was developed in the early 1980s (Ganter and Wille, 1999), has evolved into an effective technique for data analysis, knowledge discovery and information retrieval. The study on expanding the theory of FCA to deal with uncertain and imperfect data has made considerable progress in recent years. In this paper, we will introduce a survey of the research papers on integrating FCA with fuzzy logic. The key goal is to investigate and compare different fuzzy FCA approaches that have been proposed and to clarify relationships between these approaches, as well as we will introduce a survey of the research papers on employing FCA with fuzzy logic in knowledge discovery in databases and data mining, information retrieval and ontology engineering.