On the connection of hypergraph theory with formal concept analysis and rough set theory

Cattaneo, Gianpiero; Chiaselotti, Giampiero; Ciucci, Davide; Gentile, Tommaso

doi:10.1016/j.ins.2015.09.054

Cited by 43 publications

(10 citation statements)

References 28 publications

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“…Information map F meets the condition F : U × E → V and specifies the attribute values of each object in the discourse domain U . For every subset of the attributes A ⊆ E , A ‐indiscernibility relation, expressed as IND( A ), is defined by the formula () 18 . In the decision table, S , x , and y are equivalent concerning B ; that is, x and y cannot be distinguished by the attributes in A .

IND ((), A) = ({}, ((), x, y) \in U \times U ∣ \forall a \in A, F ((), x, a) = F ((), y, a)) .

…”

Section: Methodsmentioning

confidence: 99%

Pattern design and optimization of yarn‐dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Zhang

Pan

Wang

et al. 2021

Color Research & Application

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Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn‐dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn‐dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.

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IND ((), A) = ({}, ((), x, y) \in U \times U ∣ \forall a \in A, F ((), x, a) = F ((), y, a)) .

…”

Section: Methodsmentioning

confidence: 99%

Pattern design and optimization of yarn‐dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Zhang

Pan

Wang

et al. 2021

Color Research & Application

View full text Add to dashboard Cite

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“…(4) Rough set technology: in the process of theory application, it is not necessary to grasp the prior conditions of the problem to determine the inherent law of the problem. Rough set technology theory is mainly applied in fuzzy recognition and decision analysis [25]. faced by enterprises, and logistics has gradually become the third source of profit for enterprises and an important area to cultivate their core competitiveness.…”

Section: System Constructionmentioning

confidence: 99%

Management Problems of Modern Logistics Information System Based on Data Mining

Shu

Huang

et al. 2021

Mobile Information Systems

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With the development of technology, the data stored by humans is growing geometrically. Especially in the logistics industry, the rise of online e-commerce has created a huge data flow in the informatized logistics network. How to collect, analyze, and organize this information in time and analyze the meaning of this information from it is a difficult problem. The paper aims to learn the management of logistics systems from the perspective of statistics. This article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. The information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. The experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits. The experimental data show that mining effective and accurate information from massive information can help companies to quickly make decisions, formulate scientific logistics management programs, improve operating efficiency, reduce operating costs, and obtain good benefits.

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“…in fields, such as medicine, economy, finance, business, environment, electrical and computer engineering. Granular computing and rough sets have been studied in context of graphs [4,5], digraph [13] and hyper graphs [2]. The reader is referred to the following [3,20,21,23] for further reading in this area of study.…”

Section: Introductionmentioning

confidence: 99%