Parallel Self-Organizing Map (Parallel-SOM) is proposed to modify Self-Organizing Map for parallel computing environments. In this model, the conventional repeated learning procedure is modified to learn just once. The once learning manner is more similar to human learning and memorizing activities. During training, every connection between neurons of input and output layers is considered as an...

A new method for image clustering with density maps derived from Self-Organizing Maps (SOM) is proposed together with a clarification of learning processes during a construction of clusters. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters ...

In many situations, high dimensional data can be considered as sampled functions. We show in this paper how to implement a Self-Organizing Map (SOM) on such data by approximating a theoretical SOM on functions thanks to basis expansion. We illustrate the proposed method on real world spectrometric data for which functional preprocessing is very successful.

The biometrics authentication systems take attentions to cover the weakness of password authentication system. In this paper, we focus attention on the multi modal-biometrics of behavior characteristics. For the integration of multi modal biometrics Supervised Pareto learning SOM(SP-SOM) and its incremental learning method for implementing adaptive authentication system are proposed. Key–Words:...

A topographic map is a two-dimensional, nonlinear approximation of a potentially high-dimensional data manifold, which makes it an appealing instrument for visualizing and exploring high-dimensional data. The Self-Organizing Map (SOM) is the most widely used algorithm, and it has led to thousands of applications in very diverse areas. In this chapter, we will introduce the SOM algorithm, discus...

It has been 17 years since the first Workshop on Self-organizing Maps (WSOM) was held in Helsinki, Finland in 1997, under the leadership of Teuvo Kohonen. The workshop brings together researchers and practitioners in the field of self-organizing systems and related areas. The 9th WSOMwas held for the first time in LatinAmerica, at the Universidad de Chile, Santiago, Chile, on December 2012. Thi...

Self-Organizing Maps (SOM) is an excellent method of analyzing multidimensional data. The SOM based classification is attractive, due to its unsupervised learning and topology preserving properties. In this paper, the performance of the self-organizing methods is investigated in induction motor rotor fault detection and severity evaluation. The SOM is based on motor current signature analysis (...

We have developed an experimental system called PicSOM for retrieving images similar to a given set of reference images in large unannotated image databases. The technique is based on a hierarchical variant of the Self-Organizing Map (SOM) called the Tree Structured Self-Organizing Map (TS-SOM). Given a set of reference images, PicSOM is able to retrieve another set of images which are most sim...

This paper adopts and adapts Kohonen’s standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The twodimensional representation of the SOTM attempts t...

The detection of ischemic episodes is a difficult pattern classification problem. The motivation for developing the Supervising Network Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications. The sNet-SOM uses unsupervised learning for the regions where the classification is not ambigu...