3 research outputs found
Drawing Order Diagrams Through Two-Dimension Extension
Order diagrams are an important tool to visualize the complex structure of
ordered sets. Favorable drawings of order diagrams, i.e., easily readable for
humans, are hard to come by, even for small ordered sets. Many attempts were
made to transfer classical graph drawing approaches to order diagrams. Although
these methods produce satisfying results for some ordered sets, they
unfortunately perform poorly in general. In this work we present the novel
algorithm DimDraw to draw order diagrams. This algorithm is based on a relation
between the dimension of an ordered set and the bipartiteness of a
corresponding graph.Comment: 16 pages, 12 Figure
Towards Ordinal Data Science
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics