Assessment of model properties with respect to data is important for reliable analysis of data. After training, Self-Organizing Map (SOM) can be assessed, for instance, with respect to its quantization or its topology preservation properties with onenumber summaries. In this paper, we present a decomposition of the SOM distortion measure for measuring different aspects of the SOM for map units ...

To keep an online game interesting to its users, it is important to know them. In this paper, in order to characterize user characteristics, we discuss clustering of online-game users based on their trails using Self Organization Map (SOM). As inputs to SOM, we introduce transition probabilities between landmarks in the targeted game map. An experiment is conducted confirming the effectiveness ...

In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In ...

In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Evolving Fuzzy Case-Based Reasoning (EFCBR) for flow time prediction is proposed. Genetic Algorithms (GAs) is applied to fine-tune the fuzzy term numbers and weights of fuzzy features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, correspondi...

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, as the original input manifold can be complicated with an inherent dimension larger than that of the feature map, the dimension reduction in SOM can be too drastic, generating a folded feature map. In order to eliminate this pheno...

We construct a benchmarking model in the form of a twodimensional self-organising map (SOM) to compare the performance of nonbanking financial institutions (NFIs) in Romania. The NFIs are characterized by a number of performance dimensions such as capital adequacy, assets’ quality and profitability. First, we apply Kohonen’ SOM algorithm (an unsupervised neural network algorithm) to group the N...

Kohonen’s self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks ...

In this study, we try to implant chaotic features into the learning algorithm of self-organizing map. We call this concept as Chaotic SOM (CHAOSOM). As a first step to realize CHAOSOM, we consider the case that learning rate and neighboring coefficient of SOM are refreshed by chaotic pulses generated by the Hodgkin-Huxley equation. We apply the CHAOSOM to solve a traveling salesman problem and ...