Characteristics and segmentation of social problems with kohonen self-organizing maps

An accelerator for Kohonen Self-Organizing Maps

In this work we are describing hardware implementation of Kohonen SelfOrganizing Map. We examined existing neurocomputers and decided to work out our own neurocomputer with a different, more suitable architecture. Our neurocomputer is being realized on FPGA (Field-Programmable Gate Array). In this article we are describing basic neurocomputer unit structure as well as linkage of these elements ...

Green Product Consumers Segmentation Using Self-Organizing Maps in Iran

This study aims to segment the market based on demographical, psychological, and behavioral variables, and seeks to investigate their relationship with green consumer behavior. In this research, self-organizing maps are used to segment and to determine the features of green consumer behavior. This was a survey type of research study in which eight variables were selected from the demographical,...

Molecular subtyping of bladder cancer using Kohonen self-organizing maps

Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to strati...

Understanding Eurasian Convergence: Application Of Kohonen Self-Organizing Maps

Generalized Winner-Relaxing Kohonen Self-Organizing Feature Maps

We calculate analytically the magnification behaviour of a generalized family of self-organizing feature maps inspired by a variant introduced by Kohonen in 1991, denoted here as Winner Relaxing Kohonen algorithm, which is shown here to have a magnification exponent of 4/7. Motivated by the observation that a modification of the learning rule for the winner neuron influences the magnification l...