Kohonen's self-organizing maps in contextual analysis of data

Kohonen's Self-organizing Map (SOM) is a means for automatically arranging high-dimensional statistical data. The map attempts to represent all the input with optimal accuracy using a restricted set of models or prototypes. The prototypes also become ordered on the map grid so that similar prototypes are close to each other and dissimilar prototypes far from each other. The SOM is useful in clustering, abstraction, and visualization through dimensionality reduction. It has been used in a multitude of application areas ranging form speech recognition to data mining of tests and form robotics to process monitoring. The unsupervised learning scheme of the SOM makes it well suited for applications in which the input data cannot be labeled. A map is ordered and it follows the patterns of the input data in a non-linear but generalizing fashion. All this makes it well suited for data analysis and many areas in developing intelligent systems. In this article, the general principles of using the SOM in data analysis are considered reflecting on the concept of context. An illustrative experiment of data analysis is presented.

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