SELF-ORGANIZING MAPS, THEORY AND APPLICATIONS
Resumen
The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo Kohonen in the early 80s. It
acts as a non supervised clustering algorithm as well as a powerful visualization tool. It is widely used in many
application domains, such as economy, industry, management, sociology, geography, text mining, etc. Many
variants have been defined to adapt SOM to the processing of complex data, such as time series, categorical data,
nominal data, dissimilarity or Kernel data. However, so far SOM had suffered from a lack of rigorous results
on its convergence and stability. This article presents the state-of-art on the theoretical aspects of SOM, as well
as several extensions to non numerical data and provides some typical examples of applications in different
real-world fields.
KEYWORDS: SOM,
acts as a non supervised clustering algorithm as well as a powerful visualization tool. It is widely used in many
application domains, such as economy, industry, management, sociology, geography, text mining, etc. Many
variants have been defined to adapt SOM to the processing of complex data, such as time series, categorical data,
nominal data, dissimilarity or Kernel data. However, so far SOM had suffered from a lack of rigorous results
on its convergence and stability. This article presents the state-of-art on the theoretical aspects of SOM, as well
as several extensions to non numerical data and provides some typical examples of applications in different
real-world fields.
KEYWORDS: SOM,
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