36,376 research outputs found
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
Decision support with data-analysis methods in a nuclear power plant
Early fault detection is an important issue in nuclear industry. Methods based on self-organizing map (SOM) in dynamic systems are discussed and developed to help operators and plant experts in their decision making and used together with other methods. Visualization issues are in an important role in this research. Prototype systems are built to be able to test the basic principles. Five different studies are presented in detail. This report summarizes the test case 4 (TC4) "Decision support at a nuclear power plant" in NoTeS and NoTeS2 projects in TEKES MASI research program
Self-Organizing Maps and Parton Distributions Functions
We present a new method to extract parton distribution functions from high
energy experimental data based on a specific type of neural networks, the
Self-Organizing Maps. We illustrate the features of our new procedure that are
particularly useful for an anaysis directed at extracting generalized parton
distributions from data. We show quantitative results of our initial analysis
of the parton distribution functions from inclusive deep inelastic scattering.Comment: 8 pages, 4 figures, to appear in the proceedings of "Workshop on
Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th
-21st, 201
Self- and Super-organizing Maps in R: The kohonen Package
In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.
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