Information flow-based second-order cone programming model for big data using rough concept lattice

Wang, Pin; Wu, Wei; Zeng, Lingyu; Zhong, Hongmei

doi:10.1007/s00521-022-07289-z

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is of great theoretical and practical importance to study the attribute reduction method of the concept lattice. This method can provide powerful tools for knowledge discovery [7][8][9], data mining [10][11][12], information retrieval [13], semantic networks [14,15], and ontology construction [16,17] based on concept lattice theory.…”

Section: Introductionmentioning

confidence: 99%

Stream Convolution for Attribute Reduction of Concept Lattices

Xu,

Wu,

et al. 2023

Mathematics

View full text Add to dashboard Cite

Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

show abstract

Section: Introductionmentioning

confidence: 99%

Stream Convolution for Attribute Reduction of Concept Lattices

Xu,

Wu,

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Li [16] presents an in-depth study and analysis of the AC drive control simulation of a supercapacitor tram using a high-order neural network pattern discrimination algorithm. Wu et al [17] study the rough concept lattice and use the information flow to construct a second-order cone programming model for big data. The relay protection characteristics of DN under DG access are analyzed by Cui et al [18].…”

mentioning

confidence: 99%