There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information ...There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information system, the realization of rough set approximation operation has two major steps, firstly convert the information system from multiple valued one to single valued formal context, secondly realize rough set approximation operations aided by concept lattice, which is equivalent to a query operation under some necessary conditions.展开更多
Identifying business components is the basis of component-based software engineering. Many approaches, including cluster analysis and concept analysis, have been proposed to identify components from business models. T...Identifying business components is the basis of component-based software engineering. Many approaches, including cluster analysis and concept analysis, have been proposed to identify components from business models. These approaches classify business elements into a set of components by analyzing their properties. However, most of them do not consider the difference in their properties for the business elements, which may decrease the ac- curacy of the identification results. Fhrthermore, component identification by partitioning business elements cannot reflect which features are responsible for the generation of certain results. This paper deals with a new approach for component identification from business models using fuzzy formal concept analysis. First, the membership between business elements and their properties is quantified and transformed into a fuzzy formal context, from which the concept lattice is built using a refined incremental algorithm. Then the components are selected from the concepts according to the concept dispersion and distance. Finally, the effectiveness and efficiency are validated by applying our approach in the real-life cases and experiments.展开更多
Non-fungible token(NFT)is a digital asset whose ownership can be validated and controlled via blockchain technology.The NFT market is a rapidly growing field,and the rarity of NFT is an essential factor that affects i...Non-fungible token(NFT)is a digital asset whose ownership can be validated and controlled via blockchain technology.The NFT market is a rapidly growing field,and the rarity of NFT is an essential factor that affects its price,as scarcity leads to higher demand.This study focuses on the BAYC NFT collection,which is a successful and representative collection of profile picture NFT,and analyzes how rarity affects NFT prices.This paper investigates the relationship between the rarity and price of the BAYC NFT collection using formal concept analysis(FCA)method.The results show that rarity is a major factor influencing the price of NFT and the effect is more apparent in the medium rarity range.When rarity is very high or very low,other factors become significant determinants,such as the uniqueness and appeal of NFT,and even the naturalness of NFT images.This research highlights the importance of considering rarity when assessing NFT and underscores the need for a comprehensive evaluation of NFT rarity.This study also provides valuable insights into the NFT market and can be useful for NFT investors,creators,and collectors.Furthermore,the usefulness of FCA as a tool for quantifying NFT rarity and evaluating NFT price was demonstrated.展开更多
Serve to introduce decision context and decision implication to Formal Concept Analysis （FCA）. Since extracting decision implications directly from decision context takes time, we present an inference rule to reduce...Serve to introduce decision context and decision implication to Formal Concept Analysis （FCA）. Since extracting decision implications directly from decision context takes time, we present an inference rule to reduce the number of decision implications. Moreover, based on the inference rule we introduce the notion of a-maximal decision implication and prove that the set of all α-maximal decision implications is α-complete and α-non-redundant. Finally, we propose a method to generate the set.展开更多
Purpose-The 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 d...Purpose-The 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/approach-The 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.Findings-An 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/implications-The 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 implications-The 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 implications-This work can be expected in all domains that require decision-making like hotel finder,restaurant recommender,recruitment of candidates,etc.Originality/value-This 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.展开更多
Because of the completeness of concept lattices, the time complexity of constructing concept lattices has become the main factor affecting the application of formal concept analysis(FCA). The key problems in the resea...Because of the completeness of concept lattices, the time complexity of constructing concept lattices has become the main factor affecting the application of formal concept analysis(FCA). The key problems in the research of concept lattices are how to improve the generation efficiency and how to reduce the space and time complexity of constructing concept lattices. So far, reviews on lattice construction algorithms have not been comprehensive. In view of this situation, we give a detailed review on two categories of construction algorithms:batch methods and incremental methods. The first category is a formal context that cannot be updated once the concept lattice has been constructed; the second category is a formal context that can be updated after a new object being added to the formal context. We briefly introduce classical and improved construction methods, illustrate the deficiencies of some algorithms and point out the improvements of the follow-up algorithms. Furthermore, we compare and discuss several key algorithms, and also pay attention to the application of concept lattices. Finally,two further research directions of concept lattices are proposed, including parallel construction methods of concept lattices and research of heterogeneous data concept lattices.展开更多
An efficient way to improve the efficiency of the applications based on formal concept analysis （FCA） is to construct the needed part of concept lattice used by applications. Inspired by this idea, an approach that ...An efficient way to improve the efficiency of the applications based on formal concept analysis （FCA） is to construct the needed part of concept lattice used by applications. Inspired by this idea, an approach that constructs lower concept semi-lattice called non-frequent concept semi-lattice in this paper is introduced, and the method is based on subposition assembly. Primarily, we illustrate the theoretical framework of subposition assembly for non-frequent concept semi-lattice. Second, an algorithm called Nocose based on this framework is proposed. Experiments show both theoretical correctness and practicability of the algorithm Nocose.展开更多
Purpose: Formal concept analysis(FCA) and concept lattice theory(CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodolo...Purpose: Formal concept analysis(FCA) and concept lattice theory(CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodology/approach: We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research(IDR) topics in Information & Library Science(LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.Findings: The CLT approach is suitable for IDR topic identification and predictions.Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.展开更多
文摘There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information system, the realization of rough set approximation operation has two major steps, firstly convert the information system from multiple valued one to single valued formal context, secondly realize rough set approximation operations aided by concept lattice, which is equivalent to a query operation under some necessary conditions.
基金supported by the Fundamental Research Funds for the Central Universities,China
文摘Identifying business components is the basis of component-based software engineering. Many approaches, including cluster analysis and concept analysis, have been proposed to identify components from business models. These approaches classify business elements into a set of components by analyzing their properties. However, most of them do not consider the difference in their properties for the business elements, which may decrease the ac- curacy of the identification results. Fhrthermore, component identification by partitioning business elements cannot reflect which features are responsible for the generation of certain results. This paper deals with a new approach for component identification from business models using fuzzy formal concept analysis. First, the membership between business elements and their properties is quantified and transformed into a fuzzy formal context, from which the concept lattice is built using a refined incremental algorithm. Then the components are selected from the concepts according to the concept dispersion and distance. Finally, the effectiveness and efficiency are validated by applying our approach in the real-life cases and experiments.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5A2A03083440).
文摘Non-fungible token(NFT)is a digital asset whose ownership can be validated and controlled via blockchain technology.The NFT market is a rapidly growing field,and the rarity of NFT is an essential factor that affects its price,as scarcity leads to higher demand.This study focuses on the BAYC NFT collection,which is a successful and representative collection of profile picture NFT,and analyzes how rarity affects NFT prices.This paper investigates the relationship between the rarity and price of the BAYC NFT collection using formal concept analysis(FCA)method.The results show that rarity is a major factor influencing the price of NFT and the effect is more apparent in the medium rarity range.When rarity is very high or very low,other factors become significant determinants,such as the uniqueness and appeal of NFT,and even the naturalness of NFT images.This research highlights the importance of considering rarity when assessing NFT and underscores the need for a comprehensive evaluation of NFT rarity.This study also provides valuable insights into the NFT market and can be useful for NFT investors,creators,and collectors.Furthermore,the usefulness of FCA as a tool for quantifying NFT rarity and evaluating NFT price was demonstrated.
文摘Serve to introduce decision context and decision implication to Formal Concept Analysis （FCA）. Since extracting decision implications directly from decision context takes time, we present an inference rule to reduce the number of decision implications. Moreover, based on the inference rule we introduce the notion of a-maximal decision implication and prove that the set of all α-maximal decision implications is α-complete and α-non-redundant. Finally, we propose a method to generate the set.
基金The authors would like to express their special thanks of gratitude to the Directorate General for Scientific Research and Technological Development(DGRSDT),for the support of this work under the subvention number C0662300 and the grant number 167/PNE.
文摘Purpose-The 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/approach-The 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.Findings-An 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/implications-The 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 implications-The 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 implications-This work can be expected in all domains that require decision-making like hotel finder,restaurant recommender,recruitment of candidates,etc.Originality/value-This 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.
基金the National Natural Science Foundation of China(No.61273328)the National Key Foundation for Exploring Scientific Instrument of China(No.51227803)
文摘Because of the completeness of concept lattices, the time complexity of constructing concept lattices has become the main factor affecting the application of formal concept analysis(FCA). The key problems in the research of concept lattices are how to improve the generation efficiency and how to reduce the space and time complexity of constructing concept lattices. So far, reviews on lattice construction algorithms have not been comprehensive. In view of this situation, we give a detailed review on two categories of construction algorithms:batch methods and incremental methods. The first category is a formal context that cannot be updated once the concept lattice has been constructed; the second category is a formal context that can be updated after a new object being added to the formal context. We briefly introduce classical and improved construction methods, illustrate the deficiencies of some algorithms and point out the improvements of the follow-up algorithms. Furthermore, we compare and discuss several key algorithms, and also pay attention to the application of concept lattices. Finally,two further research directions of concept lattices are proposed, including parallel construction methods of concept lattices and research of heterogeneous data concept lattices.
基金Supported by the National Natural Science Foundation of China (60970018)the Fundamental Research Funds for the Central Universities
文摘An efficient way to improve the efficiency of the applications based on formal concept analysis （FCA） is to construct the needed part of concept lattice used by applications. Inspired by this idea, an approach that constructs lower concept semi-lattice called non-frequent concept semi-lattice in this paper is introduced, and the method is based on subposition assembly. Primarily, we illustrate the theoretical framework of subposition assembly for non-frequent concept semi-lattice. Second, an algorithm called Nocose based on this framework is proposed. Experiments show both theoretical correctness and practicability of the algorithm Nocose.
基金an outcome of the project "Study on the Recognition Method of Innovative Evolving Trajectory based on Topic Correlation Analysis of Science and Technology" (No. 71704170) supported by National Natural Science Foundation of Chinathe project "Study on Regularity and Dynamics of Knowledge Diffusion among Scientific Disciplines" (No. 71704063) supported by National Natura Science Foundation of Chinathe Youth Innovation Promotion Association, CAS (Grant No. 2016159)
文摘Purpose: Formal concept analysis(FCA) and concept lattice theory(CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodology/approach: We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research(IDR) topics in Information & Library Science(LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.Findings: The CLT approach is suitable for IDR topic identification and predictions.Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.
