Appropriate Use of Self-Organizing Maps as a Data Mining Technique for Discovering Information

Abstract

The study approaches the appropriate use of Self-Organizing Maps for the discovery of nontrivial information as an unsupervised clustering technique to discriminate unlabelled collected data. Some examples of its correct use reveal its potential to study large amount of data. Likewise, these outcomes are contrasted with situations that show a misuse of this tool leading to inconsistent, trivial, distorted and inadequate results, which detract from the essence of these techniques in finding information.