Concept Learning from Triadic Data

Zhuk, Roman; Ignatov, Dmitry I.; Konstantinova, Natalia

doi:10.1016/j.procs.2014.05.345

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…A detailed yet retrospective survey on JSM-method (in FCA-based and original formulation) and its applications can be found in [14]. A further extension of JSM-method to triadic data with target attribute in FCA-based formulation can be found in [130,131]; there, the triadic extension of JSM-method used CbO-like algorithm for classification in Bibsonomy data.…”

Section: Jsm-methods Of Hypothesis Generationmentioning

confidence: 99%

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Ignatov

2015

Communications in Computer and Information Science

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Abstract. This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

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Section: Jsm-methods Of Hypothesis Generationmentioning

confidence: 99%

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Ignatov

2015

Communications in Computer and Information Science

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“…Tensor clustering is another way to find dense patterns; this approach is very similar to multimodal clustering in n-ary relations, especially in case of Boolean tensors, which normally represent nary relations between entities [40,64,61,79]. An interesting issue here, whether it is possible to obtain improvements in classification accuracy for tensors with labeled objects from one of their dimensions over conventional object-attribute representations [93].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Clustering for Community Detection

Ignatov

Semenov

Komissarova

et al. 2017

Formal Concept Analysis of Social Networks

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Multimodal clustering is an unsupervised technique for mining interesting patterns in n-adic binary relations or n-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed nsets for n-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OAbiclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large realworld networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding n-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.

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“…Portanto, (g, m, b) ∈ Y pode ser lido como "o objeto g tem o atributo m sob a condição b". Um exemplo de contexto triádico está representado na Tabela I, que contém 2 ciclos de tratamento com 3 atributos cada (a1, a2, a3) [Zhuk et al 2014]. Pode-se descrever dois tipos de regras de associação triádicas que são ser extraídas do contexto K := (G, M, B, Y ) [Biedermann 1997]: Biedermann Conditional Attribute Association Rule (BCAAR) e Biedermann Attributional Condition Association Rule (BACAR) [Zhuk et al 2014].…”

Section: Introductionunclassified

Study of the evolution of antiemetic treatment through the application of Triadic Formal Concept Analysis

Ferreira

Nobre

Zárate

et al. 2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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Cancer treatment is always improving with new techniques and drugs, applied in a process with rigorous medical analysis. One way to analyze medical outcomes is to test a new drug in one group and placebos in another. In this article, we used a database with the results of a new drug for preventing chemotherapy-induced nausea and vomiting. After processing the database, we apply the Triadic Formal Concept approach to extract triadic rules, implications, and conditions that are used to identify correlations throughout the medical sessions. Our study shows that triadic analysis can help scientists perform clinical analyzes efficiently by looking at patient history data.

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Concept Learning from Triadic Data

Cited by 4 publications

References 17 publications

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Multimodal Clustering for Community Detection

Study of the evolution of antiemetic treatment through the application of Triadic Formal Concept Analysis

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