A hierarchical self-organizing feature map for analysis of not well separable clusters of different feature density

The Time Adaptive Self Organizing Map for Distribution Estimation

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

A self-organizing map for analysis of high-dimensional feature spaces with clusters of highly differing feature density

Landforms identification using neural network-self organizing map and SRTM data

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...

Steel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps

Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...

Self Organizing Neural Networks perform different from statistical k-means clustering

If Self Organizing Feature Maps (SOFMs) are enhanced by specially designed visualization algorithms like U-Matrix methods, they can be used for clustering. A common belief is, that the clustering abilities of this type of Artificial Neural Networks is identical or at least similar to the k-means algorithm used in statistics. In this paper we show by means of an nonlinear separable dataset, that...