Randomized Self-Organizing Map

Classification of Streaming Fuzzy DEA Using Self-Organizing Map

The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on ...

Lifestyle patterns in the Iranian population: Self- organizing map application

Background: The present study evaluated the lifestyle behavior patterns and its associations with demographic factors in the Iranian population. Methods: A total of 8244 people aged 25-70 years who participated in a national survey in 2011 were included in the study. Factors related to lifestyle (such as diet, physical activity, and tobacco use) have been collected using a questionnaire. A sel...

Self-Organizing Map Convergence

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. They represent powerful data analysis tools applied in many different areas including areas such as biomedicine, bioinformatics, proteomics, and astrophysics. We maintain a data analysis package in R based on self-organizing maps. The package supports efficient, statistical measures that enable the u...

Self-organizing map

The Self-organizing Map

information from multidimensional primary signals, and to represent it as a location, say, in a two-dimensional network. Although this i s already a step towards generalization and symbolism, it must be admitted that the extraction of features from geometrically or physically relatable data elements i s still a very concrete task, in principle at least. Theoperation of the brain at the higher l...