Dynamic self-organising map

A novel self-organising clustering model for time-event documents

Purpose Neural document clustering techniques, e.g., self-organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity. However, the quantity of text is ever-increasing. We propose a novel dynamic adaptive self-organising hybrid (DASH) model, which adapts to time-event news collections not only to the neural topological structure but al...

A Self-Organising Hybrid Model for Dynamic Text Clustering

A text clustering neural model, traditionally, is assumed to cluster static text information and represent its inner structure on a flat map. However, the quantity of text information is continuously growing and the relationships between them are usually complicated. Therefore, the information is not static and a flat map may be not enough to describe the relationships of input data. In this pa...

Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

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 ...

Motive Identification in 22 Folksong Corpora Using Dynamic Time Warping and Self Organizing Maps

A system for automatic motive identification of large folksong corpora is described in this article. The method is based on a dynamic time warping algorithm determining inherent repeating elements of the melodies and a self-organizing map that learns the most typical motive contours. Using this system, the typical motive collections of 22 cultures in Eurasia have been determined, and another gr...

The self-organising map, robustness, self-organising criticality and power law