The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the " Workshop on Self-Organizi...

We propose a way of creating product maps with self-organizing maps (SOMs) for purchase decision making. We previously proposed a way of purchase decision support using SOMs and the Analytic Hierarchy Process (AHP). We provided several class boundaries, which divided the input features into several classes before creating self-organizing product maps. Because the number of classes and their bou...

Previously, we proposed an approach for corporate decision making with self-organizing patent maps labeled by technical terms and AHP. First, we extracted keywords by text mining to transform patent documents into feature vectors of the companies. Second, we inputted the feature matrix of technical terms and company names into self-organizing maps to create patent maps labeled by the technical ...

Image segmentation is a very crucial step in the field of image processing which helps us to simplify the representation of the image, to make it easier to analyze. This paper deals with the comparison of image segmentation techniques based on unsupervised artificial neural network technique, known as Kohonen’s Self Organizing Maps (SOM). We first present image segmentation using Kohonen’s Self...

An important issue in data-mining is to find effective and optimal forms to learn and preserve the topological relations of highly dimensional input spaces and project the data to lower dimensions for visualization purposes. In this paper we propose a novel ensemble method to combine a finite number of Self Organizing Maps, we called this model Fusion-SOM. In the fusion process the nodes with s...

A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional ...

Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for visualization of high-dimensional (input) data in a lower-dimensional (target) space. This algorithm is based on adaptation of parameters in local neighborhoods and therefore does not lead to the best global visualization of the input space data clusters. SOFM is compared here with alternative methods of global visualizatio...

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Every two years, the “Workshop on Self-Organizin...