Explorative data analysis based on self-organizing maps and automatic map analysis

Authors:
Marc Franzmeier;Ulf Witkowski;Ulrich Rückert
Affiliations:
Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany;Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany;Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 4
Cited 1

The scientist and engineer's guide to digital signal processing

The scientist and engineer's guide to digital signal processing
Schematizing Maps: Simplification of Geographic Shape by Discrete Curve Evolution

Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications
Tree-structured Partitioning Based on Splitting Histograms of Distances

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Hardware Accelerated Data Analysis

PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering

Visualizing high-dimensional input data with growing self-organizing maps

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the field of explorative data analysis self-organizing maps have been used successfully for a lot of applications. In our case, we apply the self-organizing map for the analysis of semiconductor fabrication data by training recorded high dimensional data sets. Usually, the training result is displayed by using appropriate visualization techniques and the results are evaluated manually. Especially for large data sets an automated post-processing of the training result is essential. In this paper an automatic training result analysis based on specific image processing is introduced. Dependencies between components maps are calculated by structure overlapping analysis based on the segmentation of component maps. This novel method has been integrated into the data analysis software DanI, that simulates self-organizing maps for data analysis with several pre-processing and post-processing capabilities.