U.S. Department of EnergyOffice of Scientific and Technical Information
Self-Organizing Maps and Their Applications to Data Analysis
Abstract
Self-Organizing Maps(SOMs) are a form of unsupervised neural network that are used for visualization and exploratory data analysis of high dimensional datasets. Our goal was to understand how we can use a SOM to gain insights about datasets. We do this by first understanding the initialization, training, error metrics, and convergence properties of the SOM. Next we discuss the ways to interpret and visualize a Self-Organizing Map. Finally we used real datasets to understand what the Self-Organizing Map can tell us about labeled and unlabeled data. Based on experiments with our datasets we found that the Self-Organizing Map can tell us about the spacing and position of high dimensional clusters, help us find non-linear patterns, and give us insight into the shape of our data.
- Authors:
-
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1566795
- Report Number(s):
- LLNL-TR-791165
989807
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Mathematics and Computing
Citation Formats
Ponmalai, Ravi, and Kamath, Chandrika. Self-Organizing Maps and Their Applications to Data Analysis. United States: N. p., 2019.
Web. doi:10.2172/1566795.
Ponmalai, Ravi, & Kamath, Chandrika. Self-Organizing Maps and Their Applications to Data Analysis. United States. https://doi.org/10.2172/1566795
Ponmalai, Ravi, and Kamath, Chandrika. 2019.
"Self-Organizing Maps and Their Applications to Data Analysis". United States. https://doi.org/10.2172/1566795. https://www.osti.gov/servlets/purl/1566795.
@article{osti_1566795,
title = {Self-Organizing Maps and Their Applications to Data Analysis},
author = {Ponmalai, Ravi and Kamath, Chandrika},
abstractNote = {Self-Organizing Maps(SOMs) are a form of unsupervised neural network that are used for visualization and exploratory data analysis of high dimensional datasets. Our goal was to understand how we can use a SOM to gain insights about datasets. We do this by first understanding the initialization, training, error metrics, and convergence properties of the SOM. Next we discuss the ways to interpret and visualize a Self-Organizing Map. Finally we used real datasets to understand what the Self-Organizing Map can tell us about labeled and unlabeled data. Based on experiments with our datasets we found that the Self-Organizing Map can tell us about the spacing and position of high dimensional clusters, help us find non-linear patterns, and give us insight into the shape of our data.},
doi = {10.2172/1566795},
url = {https://www.osti.gov/biblio/1566795},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 24 00:00:00 EDT 2019},
month = {Tue Sep 24 00:00:00 EDT 2019}
}