It has been shown earlier that the Self-Organizing Map (SOM) can be applied to the analysis and visualization of similarities of words in their usage in short contexts formed of adjacent words. These SOMs of words, called word category maps, have many potential applications. One of the application areas is information retrieval and data mining of textual document collections where the word cate...

Motivated by a motion detection system the application of a new category of Kohonen‘s Self-Organizing Map (SOM) [8] is presented in this paper. Based on earlier work of Seiffert and Michaelis on three-dimensional SOMs [10], [11] a new implementation of growing multi-dimensional SOMs, the Quasi-Four-Dimensional Neuroncube (QFDN), has already been introduced in [12]. This paper focuses on the app...

The Spatial Semantic Hierarchy (SSH) is a multi-level representation of the cognitive map used for navigation in largescale space. We propose a method for learning a portion of this representation, specifically, the representation of views in the causal level of the SSH using self-organizing neural networks (SOMs). We describe the criteria that a good view representation should meet, and why SO...

Self-Organizing Maps (SOMs) are extensively used for data clustering and dimensionality reduction. However, if applications to fully benefit from SOM based techniques, high-speed processing is demanding, given that tends be both highly dimensional yet “big”. Hence, a parallel architecture the introduced optimize system’s time. Unlike most literature approaches, proposed here does not contain se...

Self-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an important and difficult problem in many Astrophysical experiments, for instance, Gamma Ray Burst classification, or gamma-hadron separation. After a ...

The emerging research interest on neural oscillation in neuroscience has resulted in an ever-increasing number of studies on various cognitive and neuro-developmental phenomena. There is, now, evidence linking brain physiological descriptions with certain phenotypes in normal and atypical behavior, involving neural oscillation. Case studies include brain disorders, such as autism and schizophre...

Kohonen maps are an efficient mechanism in signal processing and data mining applications. However, all the existing versions and approaches of this special type of neural networks are still incapable to efficiently handle within a simple, fast, and unified framework, the imperfection of the patterns’ information elements on the one hand like the uncertainty, the missing data, etc., and the het...

The recent advances of genome-scale sequencing and array technologies have made it possible to monitor simultaneously the expression pattern of thousands or tens of thousands of genes. One of the following steps is to discover or extract the information for the genetic networks by analyzing such massive data sets. Therefore, various clustering methods, such as hierarchical clustering [3] or sel...

Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We propose two new methods for depicting the SOM based on vector fields, namely the Gradient Field and Borderline visualization techni...

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by Teuvo Kohonen. In this paper, we propose a method of using simultaneously two kinds of SOMs whose features are different. Namely, one is distributed in the area on which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOMs f...