The Kohonen self-organizing map method: An assessment |
| |
Authors: | F Murtagh M Hernández-Pajares |
| |
Institution: | 1. Space Telescope-European Coordinating Facility, European Southern Observatory, Karl-Schwarzschild-Str. 2, D-85748, Garching, Germany 2. Department de Matemàtica Aplicada i Telemàtica, Universitat Politècnica de Catalunya, Apartat 300002, E-08080, Barcelona, Spain
|
| |
Abstract: | The “self-organizing map” method, due to Kohonen, is a well-known neural network method. It is closely related to cluster
analysis (partitioning) and other methods of data analysis. In this article, we explore some of these close relationships.
A number of properties of the technique are discussed. Comparisons with various methods of data analysis (principal components
analysis, k-means clustering, and others) are presented.
This work has been partially supported for M. Hernández-Pajares by the DGCICIT of Spain under grant No. PB90-0478 and by a
CESCA-1993 computer-time grant. Fionn Murtagh is affiliated to the Astrophysics Division, Space Science Department, European
Space Agency. |
| |
Keywords: | Partitioning Optimization Dimensionality reduction Data display Exploratory data analysis |
本文献已被 SpringerLink 等数据库收录! |
|