The connections between three-way and classical concept lattices

Qi, Jianjun; Qian, Ting; Ling, Wei

doi:10.1016/j.knosys.2015.08.006

Cited by 204 publications

(48 citation statements)

References 27 publications

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“…Recently, three-way concept lattice is studied in various research fields for knowledge processing tasks. As for example three-way formal concept analysis [11], its connection with classical concept lattice [12], three way decision space [20], as well as its partial ordering using hesitant fuzzy sets [21]. Further extensive study is reported on defining complex neutrosophic set [27], shadow set [28], neutrosophic logic [29], neutrosophic graph [30], its partial ordering [21] and approximation [19] with its applications [32] in various fields [2].…”

Section: Three-way Fuzzy Concept Lattice Representation Using Neutrosmentioning

confidence: 99%

“…Recently these extensions of FCA and their future trends are analysed based on their suitable applicability in a defined universe [2]. Recently the research trends to analysis of three-way formal concept analysis [11], its connection with classical concept lattice [12], multi-scaled concept lattice [13], triadic-decision context [14], threeway incomplete context [15,16], three-way cognitive concept learning [17] at different granulation [18]. Further three-way decision space [19], with fuzzy sets [20], hesitant fuzzy sets [21] and their knowledge reduction [22] is also studied.…”

Section: Introductionmentioning

confidence: 99%

“…The properties of fuzzy concept lattice is applied in various fields for knowledge processing task [2] with augmentation in interval-valued [23] and bipolar [37] fuzzy concept lattice. Recently, three-way formal concept analysis [11], its connection with classical concept lattice [12] is studied. Further three-way decision space with fuzzy sets [20], their partial ordering [21], and approximation [19] is defined based on acceptation, rejection and uncertain regions.…”

Section: Introductionmentioning

confidence: 99%

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Three-way fuzzy concept lattice representation using neutrosophic set

Singh

2016

Int. J. Mach. Learn. & Cyber.

110

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Recently, three-way concept lattice is studied to handle the uncertainty and incompleteness in the given attribute set based on acceptation, rejection, and uncertain regions. This paper aimed at analyzing the uncertainty and incompleteness in the given fuzzy attribute set characterized by truth-membership, indeterminacy-membership, and falsity membership functions of a defined single-valued neutrosophic set. For this purpose a method is proposed to generate the component wise three-way formal fuzzy concept and their hierarchical order visualization in the fuzzy concept lattice using the properties of neutrosophic graph, neutrosophic lattice, and Gödel residuated lattice with an illustrative example.

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Section: Three-way Fuzzy Concept Lattice Representation Using Neutrosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-way fuzzy concept lattice representation using neutrosophic set

Singh

2016

Int. J. Mach. Learn. & Cyber.

110

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“…These two parts are used to express the semantics 'jointly possessed' and 'jointly not possessed' in a formal context." [25] This newly proposed three-way concept combines inclusion method (positive region) with exclusion method (negative region). Thus based on a three-way concept, one can divide http://dx.doi.org/10.1016/j.knosys.2016.01.045 0950-7051/© 2016 Elsevier B.V. All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

The attribute reductions of three-way concept lattices

Ren

Ling

2016

Knowledge-Based Systems

Self Cite

124

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Object-induced three-way object concept Attribute-induced three-way attribute concept Discernibility attribute set a b s t r a c t Three-way concept analysis is a newly proposed area of formal concept analysis from which one can obtain both the inclusion decision and the exclusion decision. In general, given a context, some attributes may not be essential in three-way concept analysis, such as forming three-way concept lattice. So in this paper, we study the attribute reductions of three-way concept lattices in order to make the data easily be understood. Firstly, based on different criteria generated from object-induced three-way concept (OEconcept), four kinds of attribute reductions are proposed. The four reductions together embody different characteristics of a formal context and can be used in different occasions. Secondly, we discuss their relationships, including their advantages and disadvantages and the relationships among consistent sets and among the cores. Thirdly, based on attribute-induced three-way concept (AE-concept), we also give four attribute-induced three-way attribute reductions and discuss their relationships. Finally, the approaches to computing these attribute reductions are presented and the obtained results are demonstrated and verified by an empirical case. In this paper, we systematically investigate the attribute reductions of threeway concept lattices which enriches the study of formal concept analysis.

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“…Currently, many kinds of concepts [18,34,45,46] have been developed to meet different requirements in the real world. It should be pointed out that these concepts have been applied in many fields such as rough analysis, rule acquisition and knowledge discovery [2,4,5,12,16,19,25,27,28,38].…”

Section: Introductionmentioning

confidence: 99%

Cognitive concept learning from incomplete information

Zhao

Liu

et al. 2016

Int. J. Mach. Learn. & Cyber.

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Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.

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The connections between three-way and classical concept lattices

Cited by 204 publications

References 27 publications

Three-way fuzzy concept lattice representation using neutrosophic set

Three-way fuzzy concept lattice representation using neutrosophic set

The attribute reductions of three-way concept lattices

Cognitive concept learning from incomplete information

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