Creating Fuzzy Concepts: the One-Sided Threshold, Fuzzy Closure and Factor Analysis Methods

Nowadays, extracting fuzzy concepts using fuzzy formal concept analysis (FFCA) is an increasingly important process in many fields including data-mining, information retrieval, and Ontology construction. However, recent studies have heightened the need for more efficient approaches for extracting a reduced count of distinct fuzzy concepts in a reasonable time. This paper aims mainly to address such challenge. Generally, the proposed approach is composed of two main stages to handle quantitative data effectively. Firstly, a data-sensitive fuzzification stage maps the many-valued context to a more consistent fuzzy one. Secondly, an enhanced algorithm generates a reduced count of valuable fuzzy concepts and merges the more similar ones. Accordingly, the proposed approach can efficiently handle very large datasets where the process of extracting all fuzzy concepts is an intractable task. Surprisingly, the proposed approach reduces the overall processing time and complexity when compared with some other previous approaches.

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“…(6) and Eq. 7present the second viewpoint that relies primarily on threshold interval [t1, t2] [19][20][21].…”

Section: Ffca Notions and Definitionsmentioning

confidence: 99%

A Data-Sensitive Approach for Fuzzy Concept Extraction

Shemis¹,

Gadallah²,

Hefny³

2018

IJIES

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“…Prade [25] Aswani Kumar Bartl et al [41] Skowron et al [44] For detailed illustrations about generating the formal concepts from a given formal context, readers can refer to references including [1][2][3][4][5][6][7][8]13,16,[24][25][26][27][29][30][31][32][33][34][35][36][37][38][39][40]. Reducing the number of formal concepts and the size of the lattice structure are open issues for researchers as knowledge reduction problems.…”

Section: Formal Concept Analysis In the Fuzzy Settingmentioning

confidence: 99%

A Note on Computing the Crisp Order Context of a Fuzzy Formal Context for Knowledge Reduction

Singh¹,

Cherukuri²

2014

J Inf Process Syst

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Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

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Creating Fuzzy Concepts: the One-Sided Threshold, Fuzzy Closure and Factor Analysis Methods

Cited by 4 publications

References 9 publications

A Data-Sensitive Approach for Fuzzy Concept Extraction

A Data-Sensitive Approach for Fuzzy Concept Extraction

A Note on Computing the Crisp Order Context of a Fuzzy Formal Context for Knowledge Reduction

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