In this study, a multistage modular self-organizing map (SOM) model is proposed for parallel web text clustering. In the first stage, the large textual datasets are divided into some small disjoint datasets (i.e., task decomposition). In the second stage, each small data set is input into different unitary SOM models for word clustering map (i.e., modularization learning). In this stage, differ...

Previous studies on the structure of emotion concepts often use multidimensional scaling (MDS) or other clustering methods to plot different emotion words upon 2D space or dendrogram, representing each emotion as a point or branch within the structure, and the categories of emotions are determined accordingly. Although there seems to offer the conceptual representations of typical or atypical e...

In the real world, it is not always true that the nextdoor house is close to my house, in other words, “neighbors” are not always “true neighbors”. In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain the m...

Clustering algorithm for the moving or trajectory data provides new and helpful information. It has wide application on various location aware services. In this study the Self Organizing Map is used to form the cluster on trajectory data. The self-organizing map (SOM) is an important tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular gri...

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are imp...

With the increase of computer usage, the number of digital documents is reaching values that make unviable conducting the tasks of text organization by humans. There is a demand for text organization tools that can operate with little human intervention and that can display the results of the organization in the most commonly used visual interface: the two-dimensional (2D) plane. One of the tec...

Image classification is an important topic in digital image processing, and it could be solved by pattern recognition methods. This paper is a survey based on Self Organising Maps used as a supervised algorithm for image classification. It is observed that SOM can be used as a supervised method, and can have better advantages: better predictions, easier to interpret and better stability. Keywor...

We propose a new method called C-SOM using a Self-Organizing Map (SOM) for function approximation. C-SOM takes care about the output values of the «win-ning» neuron's neighbors of the map to compute the output value associated with the input data. Our work extends the standard SOM with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve its gen...

In this article, the use of the self-organizing map (SOM) is approached on the basis of current theories of learning. Possibilities of computer and networked platforms that aim at helping human learning are also inspected. It is shown how the SOM can be considered a model of constructive learning. The area of constructive learning is outlined and two cases of using the self-organizing map in co...

The application of a hierarchical self{organizing map (HSOM) to the problem of segmentation of multispectral magnetic resonance (MR) images is investigated. The HSOM is composed of several layers of the self{organizing map (SOM) organized in a pyramidal fashion. The SOM has been used for the segmentation of multispectral MR images but the results often su er from undersegmentation and oversegme...