Self-Organizing Maps (SOMs) are a prominent tool for exploratory data analysis. One core task within the utilization of SOMs is the identification of the cluster structure on the map for which several visualization methods have been proposed, yet different application domains may require additional representation of the cluster structure. In this paper, we propose such a method based on pairwise distance calculation. It can be plotted on top of the map lattice with arrows that point to the closest cluster center. A parameter is provided that determines the granularity of the clustering. We provide experimental results and discuss the general applicability of our method, along with a comparison to related techniques
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Often in the context of multidimensional data, there is a need to analyze the clusters, find similar...
Abstract. The use of self-organizing maps to analyze data often depends on finding effective methods...
Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, a...
Abstract. The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, i...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
. The Self-Organizing Map (SOM) can be used for forming overviews of multivariate data sets and for ...
Self-Organizing Maps are a prominent tool for exploratory analysis and visualization of high-dimensi...
We will show that the number of output units used in a self-organizing map (SOM) influences its appl...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is one of the artificial neural networks that perform vector quantizat...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Often in the context of multidimensional data, there is a need to analyze the clusters, find similar...
Abstract. The use of self-organizing maps to analyze data often depends on finding effective methods...
Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, a...
Abstract. The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, i...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constrai...
. The Self-Organizing Map (SOM) can be used for forming overviews of multivariate data sets and for ...
Self-Organizing Maps are a prominent tool for exploratory analysis and visualization of high-dimensi...
We will show that the number of output units used in a self-organizing map (SOM) influences its appl...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
A powerful method in the analysis of datasets where there are many natural clusters with varying sta...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is one of the artificial neural networks that perform vector quantizat...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capa...
Often in the context of multidimensional data, there is a need to analyze the clusters, find similar...
Abstract. The use of self-organizing maps to analyze data often depends on finding effective methods...