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Found 9 papers, 2 papers with code
Towards Ordinal Data Science
One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations.
Maximal Ordinal Two-Factorizations
Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i. e., a part of the dataset that corresponds to a linear order.
Greedy Discovery of Ordinal Factors
Based on such an ordinal factorization, we provide a way to discover and explain relationships between different items and attributes in the dataset.
Discovering Locally Maximal Bipartite Subgraphs
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs.
Attribute Selection using Contranominal Scales
One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties.
Neural Networks for Semantic Gaze Analysis in XR Settings
Virtual-reality (VR) and augmented-reality (AR) technology is increasingly combined with eye-tracking.
Force-Directed Layout of Order Diagrams using Dimensional Reduction
Order diagrams allow human analysts to understand and analyze structural properties of ordered data.
Computational Geometry Combinatorics 68R10, 06A07 G.2.2
FCA2VEC: Embedding Techniques for Formal Concept Analysis
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets.
DimDraw -- A novel tool for drawing concept lattices
Concept lattice drawings are an important tool to visualize complex relations in data in a simple manner to human readers.
