On Invariance of Concept Stability for Attribute Reduction in Concept Lattice

Hao, Fei; Yang, Erhe; Guo, Lanhui; Nasridinov, Aziz; Park, Doo-Soon

doi:10.1007/978-981-15-9343-7_14

Cited by 4 publications

(2 citation statements)

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“…Constraining attribute creation to sequential observations is precisely one of the pillars of the methodology presented here. Despite the above and more recent proposals to improve the computing efficiency (Mouakher et al, 2021), add flexibility (Min and Kim, 2019) and reduce complexity in concept lattices (Aragón et al, 2021;Hao et al, 2021), the literature throughout the last years is sparse on FCA studies engaging with continuous data except for a few examples such as (Boukhetta et al, 2020), where FCA is used to mine sequential patterns in interval-based sequences.…”

Section: Formal Concept Analysismentioning

confidence: 99%

Concept Emergence from Complex Sensory Data: A Connectionist Model

de Miguel Rodríguez

2023

Preprint

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Section: Formal Concept Analysismentioning

confidence: 99%

Concept Emergence from Complex Sensory Data: A Connectionist Model

de Miguel Rodríguez

2023

Preprint

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“…Electronic commerce systems are complex, highly concurrent, and distributed. The formal analysis method is an efficient knowledge representation and discovery tool, such as formal concept analysis (FCA) [12]. Data errors and state inconsistencies are often inseparable from the abnormal events in an online shopping system [13].…”

Section: Introductionmentioning

confidence: 99%

A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

Wang

Teng

et al. 2022

Wireless Communications and Mobile Computing

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With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

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