Name
Abstract | ||
|---|---|---|
In recent years, a variety of visualization techniques for visual data exploration based on self-organizing maps (SOMs) have been developed. To support users in data exploration tasks, a series of software tools emerged which integrate various visualizations. However, the focus of most research was the development of visualizations which improve the support in cluster identification. In order to provide real insight into the data set it is crucial that users have the possibility of interactively investigating the data set. This work provides an overview of state-of-the-art software tools for SOM-based visual data exploration. We discuss the functionality of software for specialized data sets, as well as for arbitrary data sets with a focus on interactive data exploration. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21566-7_18 | WSOM |
Keywords | Field | DocType |
visual interactive data exploration,som-based visual data exploration,cluster identification,software tool,self-organizing map,visual data exploration,arbitrary data set,data exploration task,specialized data set,interactive data exploration,state-of-the-art software tool | Data science,Interaction technique,Data set,Feature vector,Data exploration,Computer science,Self-organizing map,Augmented reality,Software,Human–computer interaction,Creative visualization | Conference |
Volume | ISSN | Citations |
6731 | 0302-9743 | 5 |
PageRank | References | Authors |
0.81 | 18 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julia Moehrmann | 1 | 24 | 2.98 |
| Andre Burkovski | 2 | 19 | 3.95 |
| Evgeny Baranovskiy | 3 | 5 | 0.81 |
| Geoffrey-Alexeij Heinze | 4 | 5 | 0.81 |
| Andrej Rapoport | 5 | 6 | 1.17 |
| Gunther Heidemann | 6 | 454 | 48.16 |