Visualization of Statistical Information in Concept Lattice Diagrams
Aus International Center for Computational Logic
Visualization of Statistical Information in Concept Lattice Diagrams
Jana KlimpkeJana Klimpke, Sebastian RudolphSebastian Rudolph
Jana Klimpke, Sebastian Rudolph
Visualization of Statistical Information in Concept Lattice Diagrams
In Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber, eds., Proceedings of the 16th International Conference on Formal Concept Analysis, volume 12733 of LNCS, 208-223, 2021. Springer
Visualization of Statistical Information in Concept Lattice Diagrams
In Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber, eds., Proceedings of the 16th International Conference on Formal Concept Analysis, volume 12733 of LNCS, 208-223, 2021. Springer
- KurzfassungAbstract
We propose a method of visualizing statistical information in concept lattice diagrams. To this end, we examine the characteristics of support, confidence, and lift, which are parameters used in association analysis. Based on our findings, we develop the notion of \emph{cascading line diagrams}, a visualization method that combines the properties of additive line diagrams with association analysis. In such diagrams, one can read the size of a concept's extent from the height of the corresponding node in the diagram and, at the same time, the geometry of the formed quadrangles illustrates whether two attributes are statistically independent or dependent and whether they are negatively or positively correlated. In order to demonstrate this visualization method, we have developed a program generating such diagrams. - Forschungsgruppe:Research Group: Computational LogicComputational Logic
@inproceedings{KR2021,
author = {Jana Klimpke and Sebastian Rudolph},
title = {Visualization of Statistical Information in Concept Lattice
Diagrams},
editor = {Agn{\`{e}}s Braud and Aleksey Buzmakov and Tom Hanika and
Florence Le Ber},
booktitle = {Proceedings of the 16th International Conference on Formal
Concept Analysis},
series = {LNCS},
volume = {12733},
publisher = {Springer},
year = {2021},
pages = {208-223},
doi = {10.1007/978-3-030-77867-5_13}
}