The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensit...

This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this p...

Knowledge discovery in ubiquitous environments are usually conditioned by the data stream model, e.g., data is potentially infinite, arrives continuously and is subject to concept drift. These factors present additional challenges to standard data mining algorithms. Artificial Neural Networks (ANN) models are still poorly explored in these settings. State-of-the-art methods to deal with data st...

This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they be arranged to best explain their genesis? The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem where only limited prior odom...

To take further steps along the path toward true artificial intelligence, systems must be built that are capable of learning about the world around them through observation and explanation. These systems should be flexible and robust in the style of the human brain and little precompiled knowledge should be given initially. As a step toward achieving this lofty goal, this thesis presents the se...

Adaptation neighborhoods of self-organizing maps for image restoration are presented in this study. Generally, self-organizing maps have been studied for the ordering process and the convergence phase of weight vectors. As a new approach of self-organizing maps, some methods of adaptation neighborhoods for image restoration are proposed. The present algorithm creates a map containing one unit f...

Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive EM algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional dat...

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,...

Abstract The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we map regions with relative content rare process ( H → WW *). Second obtain Monte Carlo normalization factors different physics processes by fitting the dimensionally reduced repr...