The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to ana...

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...

The self-organizing map (SOM) converts statistical relationships between highdimensional data into geometric relationships on a low-dimensional grid. It can thus be regarded as a projection and a similarity graph of the primary data. As it preserves the most important topological relationships of the data elements on the display, it may be thought of as producing some form of abstraction. These...

It has been shown that self-organized maps, when adequately trained with the set of integers 1 to 32, lay out real numbers in a 2D map in an ordering that is superior to any of the known 2D orderings, such as the Cantor-diagonal, Morton, Peano-Hilbert, raster-scan, row-prime, spiral, and random orderings. Two 2D order metrics (Average Direct Neighbor Distance and Average Unit Disorder) have bee...

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...

Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to Chen’s fuzzy back propagation network (FBPN) in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Examples belonging to di...

To further enhance the accuracy of lot output time prediction in a wafer fab (fabrication plant), a hybrid artificial neural network is proposed in this study. At first, the concept of input classification is applied to Chen’s fuzzy back propagation network (FBPN) by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Then, examples ...

Here a new algorithm based on the Self Organizing Map is presented which allows for easier categorization of textual data. The algorithm is based heavily on the concept of metadata and its extraction and providing an abstract look at that data. The algorithm is titled the Abstract Self Organizing Map for that reason. The paper discusses the current and potential impact of metadata, the current ...

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...