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Scientific communication encompasses various types and forms of communication conducted through the use of communication methods and tools, aiming to exchange scientific knowledge and information. To gain a comprehensive understanding of scientific and research communications and enhance them, it is crucial to identify the terms and concepts. Therefore, the main objective of this research is to identify and conceptually cluster key terms in the field of scientific communication using text mining techniques. The present research method is quantitative in terms of approach and practical in terms of purpose and utilized various text mining techniques for identifying and clustering key terms in the field of scientific communication. The research population consists of abstracts of articles related to scientific communication, extracted from databases such as Web of Science and Scopus, totaling 558 articles. The sampling method was census. Initially, all nominal phrases were extracted using available libraries. Each compound phrase was decomposed into its constituent words, and based on GloVe dictionary, the average vectors of those words were calculated, assigning a numerical vector to each compound phrase. The researchers created an equivalent expression using existing vocabulary to describe unknown terms that did not exist in the GloVe dictionary. The clustering (using the K-means method) was performed on these vectors. The findings revealed that out of 17,930 extracted keywords, 13,651 terms were noun phrases. Also, 16% of terms in the field of scientific communication were single words and 84% of them were compound. After creating vectors of compound terms and performing clustering, 40 conceptual clusters were created from 792 phrases or terms in the field of scientific communication. After adjusting and removing weak clusters, researchers finally identified 22 clusters in the field of scientific communication. Identifying the concepts and components in scientific communication in the form of conceptual clusters and its elements is attributed to the results of this research. One of the most significant findings was the assignment of numerical vectors to composite phrases based on the vectors of their constituent words. These vectors were then used for clustering and categorizing phrases, as well as improving and correcting some clusters. This method pays attention to the semantics aspects and learning in the clustering and categorization of concepts and, will aid to precise analysis of key terms and phrases in various fields. [ABSTRACT FROM AUTHOR]

Formal Concept Analysis (FCA) is a key tool in data analysis and knowledge discovery, yet its application is challenged by the complexity of concept lattices in large datasets. This paper presents the Kernel Concept Set Approach (KCS), a novel methodology that overcomes the limitations of traditional lattice reduction techniques by integrating a flexible derivation cost function and focusing on the frequency and structural importance of concepts. Unlike conventional methods, KCS efficiently operates in a general metric space, reducing computational costs and providing a dynamic approach to conceptual clustering. A comparative study with the K-means Dijkstra on Lattice (KDL) method highlights KCS's superiority in simplifying lattice complexity and enhancing clustering quality. KCS not only maintains crucial data structures but also facilitates the approximation of formal concept lattices, establishing it as an efficient alternative for structured data analysis. [ABSTRACT FROM AUTHOR]

A number of real-world problems of automatic grouping of objects or clustering require a reasonable solution and the possibility of interpreting the result. More specific is the problem of identifying homogeneous subgroups of objects. The number of groups in such a dataset is not specified, and it is required to justify and describe the proposed grouping model. As a tool for interpretable machine learning, we consider formal concept analysis (FCA). To reduce the problem with real attributes to a problem that allows the use of FCA, we use the search for the optimal number and location of cut points and the optimization of the support set of attributes. The approach to identifying homogeneous subgroups was tested on tasks for which interpretability is important: the problem of clustering industrial products according to primary tests (for example, transistors, diodes, and microcircuits) as well as gene expression data (collected to solve the problem of predicting cancerous tumors). For the data under consideration, logical concepts are identified, formed in the form of a lattice of formal concepts. Revealed concepts are evaluated according to indicators of informativeness and can be considered as homogeneous subgroups of elements and their indicative descriptions. The proposed approach makes it possible to single out homogeneous subgroups of elements and provides a description of their characteristics, which can be considered as tougher norms that the elements of the subgroup satisfy. A comparison is made with the COBWEB algorithm designed for conceptual clustering of objects. This algorithm is aimed at discovering probabilistic concepts. The resulting lattices of logical concepts and probabilistic concepts for the considered datasets are simple and easy to interpret. [ABSTRACT FROM AUTHOR]

A number of real-world problems of automatic grouping of objects or clustering require a reasonable solution and the possibility of interpreting the result. More specific is the problem of identifying homogeneous subgroups of objects. The number of groups in such a dataset is not specified, and it is required to justify and describe the proposed grouping model. As a tool for interpretable machine learning, we consider formal concept analysis (FCA). To reduce the problem with real attributes to a problem that allows the use of FCA, we use the search for the optimal number and location of cut points and the optimization of the support set of attributes. The approach to identifying homogeneous subgroups was tested on tasks for which interpretability is important: the problem of clustering industrial products according to primary tests (for example, transistors, diodes, and microcircuits) as well as gene expression data (collected to solve the problem of predicting cancerous tumors). For the data under consideration, logical concepts are identified, formed in the form of a lattice of formal concepts. Revealed concepts are evaluated according to indicators of informativeness and can be considered as homogeneous subgroups of elements and their indicative descriptions. The proposed approach makes it possible to single out homogeneous subgroups of elements and provides a description of their characteristics, which can be considered as tougher norms that the elements of the subgroup satisfy. A comparison is made with the COBWEB algorithm designed for conceptual clustering of objects. This algorithm is aimed at discovering probabilistic concepts. The resulting lattices of logical concepts and probabilistic concepts for the considered datasets are simple and easy to interpret.

Conceptual clustering is one of the key approaches for automatic concept generation from input contexts. In this paper, we propose an extension of the dominating k-means method. The proposed method introduces a flexible distance metric that enables the approximation of both Euclidean and meet(or join)-based similarity calculations. To increase the approximation accuracy, the method combines the k-means method with a Quality Threshold component. The paper shows that the method can also be used for approximation of formal concept lattices. Based on the performed tests, the method provides an efficient alternative conceptual clustering approach. • Novel extension of the k-means-based conceptual clustering algorithm. • Novel parameterized distance metric to cover different aggregation approaches. • Tool for flexible concept set reduction in formal concept analysis. [ABSTRACT FROM AUTHOR]

In human concept learning, people can naturally combine a handful of labeled data with abundant unlabeled data when they make classification decisions, which is also known as semi-supervised learning (SSL) in machine learning. Especially, human concept learning not only is a static process in human cognition but also can vary gradually with dynamic environments. Nevertheless, the classical SSL algorithms must be redesigned to accommodate newly input data. In this sense, concept-cognitive learning may be a good choice, as it can implement dynamic processes by imitating human cognitive processes. Meanwhile, numerous SSL methods were designed based on the feature vector information of instances, while ignoring concept structural information that is a very important process in human knowledge organization. Based on this idea, a novel SSL method, named semi-supervised concept learning method (S2CL), is proposed for dynamic SSL by employing concept spaces, in which knowledge is represented by hierarchical concept structures. Moreover, to make full use of the global and local conceptual information, we further propose an extended version of S2CL (namely, $\text{S2CL}^{\alpha }$ S2CL α ) for concept learning. More specifically, to effectively exploit the unlabeled data, this paper first shows some new related theories for S2CL (or $\text{S2CL}^{\alpha }$ S2CL α ) based on a regular formal decision context; then a novel SSL framework is designed, and its corresponding algorithm is developed. Finally, we conduct some experiments on various datasets to demonstrate the effectiveness of our methods, which include concept classification and incremental learning under a large quantity of unlabeled data. [ABSTRACT FROM AUTHOR]

The increasing complexity of decision-making environments has led to a rise in the involvement of decision-makers (DMs) in group decision-making problems. Clustering is widely used in large-scale group decision-making (LSGDM) to categorize DMs into smaller groups. Ensuring reasonable decision-making results requires providing explanations for the generated groups during the clustering process. To address the clustering problem in LSGDM within uncertain linguistic environments, this paper proposes a conceptual clustering method based on the linguistic concept lattice. The method efficiently manages comparable and incomparable linguistic information. To achieve interpretable clustering results for DMs, attribute and expert induction matrices are first introduced. Cluster stability analysis is then employed to automatically determine the optimal number of clusters. Second, linguistic truth-valued aggregation operators are proposed to aggregate the linguistic evaluation information of DMs in each cluster. In addition, a consensus reaching process is conducted within each cluster, and a feedback mechanism is established to iteratively update clusters when consensus cannot be reached. Finally, numerical examples and comparative analyses are presented that verify the effectiveness of the proposed approach in effectively addressing the LSGDM problem within uncertain linguistic environments. • Using conceptual clustering for the LSGDM problem. • Linguistic concepts are provided as explanations for the clustering results. • The comparable and incomparable linguistic information is handled in LSGDM. • A novel linguistic concept lattice-based LSGDM approach is proposed. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL), as a cognitive process, is an emerging field of simulating the human brain to learn concepts in the formal context. Simultaneously, attention is a core property of all perceptual and cognitive operations. Nevertheless, no current existing CCL models and conceptual clustering methods consider the impact of attention. In light of these observations, in this article, we present a novel concept learning method, called the multi-attention concept-cognitive learning model (MA-CLM), to address the issue by exploiting graph attention and the graph structure of the concept space. This model is deployed toward the goal of conceptual cognitive more reasonable: generate pseudo-concept with higher expected utility while taking into consideration making classification tasks more efficient. Specifically, a conceptual attention space is learned for each decision class via attribute attention. Furthermore, a new concept clustering and concept generation method based on graph attention was proposed based on the conceptual attention space. Comparative studies with S2CL α over a total of nine UCI data sets validate the effectiveness and efficiency of concept clustering based on graph attention in concept-cognitive learning. In addition, we also performed a comparative evaluation of MA-CLM against several classical classification algorithms to demonstrate the excellent properties in classification tasks. Finally, the model is validated by concept generation on the handwritten numeral dataset MNIST. [ABSTRACT FROM AUTHOR]