• Corresponding Author. • This research is support in part by a grant from the Dean’s Award for Excellence Summer Research Grants program at Arizona State University, College of Business. Abstract Kohonen's self-organizing map (SOM) network is one of the most important network architectures developed during the 1980's. The main function of SOM networks is to map the input data from an n-dimensi...

SOM O.D.I.S.S.E.A.S is an intelligent searching tool using the SelfOrganizing Map (SOM) algorithm, as a prototype e-content retrieval tool. The proposed searching tool has the ability to adjust and scale into any e-learning system that requires concept-based queries. In the proposed methodology, maps are used for the automatic replacement of the unstructured, the half structured and the multidi...

The simultaneous treatment of two interrelated and well-known tasks from strategic marketing planning, namely the determination of competitive market structure (CMS) and market segmentation, is addressed via application of the ”Self-Organizing (Feature) Map” (SOM) methodology, as originally proposed by Kohonen (1982). In the present paper, some major aspects of the methodological basis of the S...

In this paper, we present a new parallel self-organizing technique for three dimensional shape reconstruction for mobile robotics. The method is based on adaptive input data decomposition, parallel shape reconstruction in decomposed clusters using Kohonen Self-Organizing Map, which creates mesh representation of the input data. Afterwards, the sub-maps are joined together and the final mesh is ...

The right pricing of products is one of the most important issues concerning the development of companies’ financial performance. Prices should be low enough to attract customers and at the same time high enough to cover all the emerged costs and expected profits. This research illustrates how self-organizing maps (SOM) can be used for pricing purposes. We show how changes in a company’s pricin...

We are interested in practical tools for the quantitative evaluation of self-organizing maps (SOMs). Recently it has been argued that any quality measure for SOMs needs to evaluate the embedding or coverage of a map as well as its topological quality. Over the years many different quality measures for self-organizing maps have been proposed. However, many of these only measure one aspect of a S...

In this paper is presented a demonstration of Kohonen's self-organizing maps, also known as SOM. Likewise is prepared a study of the functioning of Kohonen's maps in one and two dimensions and the most important characteristics of this type of network that works in similar way that the human brain. Finally, this paper details the characteristics necessaries for the network's training and how is...

We transfer the idea of winner relaxing learning from the self-organizing map to the neural gas to enable magnification control independently of the shape of the data distribution.

Time information of the input data is used for evaluating the goodness of the Self-Organizing Map to store and represent temporal feature vector sequences. A new node neighborhood is defined for the map which takes the temporal order of the input samples into account. A connection is created between those two map nodes which are the best-matching units for two successive input samples in time. ...