Talks and Poster Presentations (with Proceedings-Entry):
G. Pölzlbauer, A. Rauber, M. Dittenbach:
"A SOM-view of Oilfield Data: A Novel Vector Field Visualization for Self-Organizing Maps and its Applications in the Petroleum Industry";
Poster: 5th International Conference on Knowledge Management, I-Know'05,
- 2005-07-01; in: "Proceedings of I-Know'05",
J.UCS - Journal of Universal Computer Science,
Self-Organizing Maps are a prominent tool for exploratory analysis and visualization of high-dimensional data. We propose a novel method for visualizing the cluster structure and coherent regions of the Self-Organizing Map that can be displayed as a vector field on top of the map lattice. Concepts of neighborhood and proximity on the map is exploited to obtain a representation where arrows point to the most similar region. The method is especially useful for large maps with a high number of map nodes. In our experiments, we visualize a data set that stems from applications in the petroleum industry, and show how to use our method to maximize the gas output.
Online library catalogue of the TU Vienna:
Project Head A Min Tjoa:
Created from the Publication Database of the Vienna University of Technology.