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

Ignatov, Dmitry I.

doi:10.1007/978-3-319-25485-2_3

Cited by 38 publications

(23 citation statements)

References 189 publications

(294 reference statements)

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“…The authors thus propose the Stability metric [22] to extract the most promising formal concepts, concluding that, if considering the external evaluation, FCA show a more homogeneous performance than the LDA and Hierarchic Agglomerative Clustering (HAC), with better overall results. Ignatov in [19] and Poelmans et al in [28] present a survey on different contributions for FCA regarding several applications. Myat and Hla [25] developed a method for web document organization based on FCA.…”

Section: Related Workmentioning

confidence: 99%

“…A concept is an association between attributes (terms or topics) that is coherently verified in a subset of objects (documents). Concept analysis has been largely pursued to explore taxonomic relationships within a corpus, addressing the typical limitations that peer unsupervised approaches face in high-dimensional and sparse spaces [19]. Formal concept analysis (FCA) aims at finding, in Boolean data spaces, concepts as subsets of topics that co-occur in a subset of documents.…”

Section: Introductionmentioning

confidence: 99%

“…Formal Concept Analysis. The theory of FCA, first introduced by Wille [33], is currently a popular method for knowledge representation [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Kovalchuk

Proença

Borbinha

et al. 2020

Lecture Notes in Computer Science

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Formal concept analysis has been largely applied to explore taxonomic relationships and derive ontologies from text collections. Despite its recognized relevance, it generally misses relevant concept associations and suffers from the need to learn from Boolean space models. Biclustering, the discovery of coherent concept associations (subsets of documents correlated on subsets of terms and topics), is here suggested to address the aforementioned problems. This work proposes a structured view on why, when and how to apply biclustering for concept analysis, a subject remaining largely unexplored up to date. Gathered results from a large text collection confirm the relevance of biclustering to find lesstrivial, yet actionable and statistically significant concept associations.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Kovalchuk

Proença

Borbinha

et al. 2020

Lecture Notes in Computer Science

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“…Many more successful applications based on FCA are known as well as related models and techniques [73,74]. A com-prehensive inroduction to FCA can be found in the recent book [26] and applicatonoriented tutorial [33].…”

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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“…For more than three decades the Formal Concept Analysis (FCA) is being developed successfully at the intersection of applied mathematics and computer science [1][2][3][4][5][6][7]. FCA has made a significant contribution and will continue to stimulate the developing of data mining, data representation and other parts of computer science due to the classical (Aristotelian) approach to the concept as the fundamental mental entity defined by the volume and content as well as to the basis of algebraic lattices theory.…”

Section: Introductionmentioning

confidence: 99%

Data Formation and Processing in Formal Concept Analysis: Subjective Aspects

Samoilov¹,

Smirnov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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Abstract. The paper gives a brief overview of the subjective aspects of data formation and processing in Formal Concept Analysis. It is shown that the fundamental cognitive scaling procedure that allows a different interpretation, introduces new information into the analysis and the analysis is not correct in the general case without paying the proper attention to this information. The relationship between the objects properties that arises from the use of various types of scales and that need to be noted, is considered.

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

Cited by 38 publications

References 189 publications

Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Multimodal Clustering for Community Detection

Data Formation and Processing in Formal Concept Analysis: Subjective Aspects

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