Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We propose two new methods for depicting the SOM based on vector fields, namely the Gradient Field and Borderline visualization techniques, to show the clustering structure at various levels of detail. We explain how this method can be used on aggregated parts of the SOM that show which factors contribute to the clustering structure, and show how to use it for finding correlations and dependencies in the underlying data. We provide examples on several artificial and real-world data sets to point out the strengths of our technique, specifically as a means to combine different types of visualizations offering effective multidimensional information visualization of SOMs.

Abstract-The Self-Organising Map is a popular unsupervised neural network model which has successfully been used for clustering various kinds of data. To help in understanding the influence of single variables or components on clusterings, we introduce a novel method for the visualisation of Component Planes for SOMs. The approach presented is based on the discretisation of the components and makes use of the wellknown metro map metaphor. It depicts consistent values and their ordering across the map for discretisations of various components and their correlations in terms of directions on the map. In our approach Component Lines are drawn for each component of the data, allowing the combination of numerous Component Planes into one plot. We also propose a method to further aggregate these Component Lines, by grouping highly correlated variables, i.e. similar lines on the map. To show the applicability of our approach we provide experimental results for two popular machine learning data sets.

Abstract. The Self-Organizing Map is one of most prominent tools for the analysis and visualization of high-dimensional data. We propose a novel visualization technique for Self-Organizing Maps which can be displayed either as a vector field where arrows point to cluster centers, or as a plot that stresses cluster borders. A parameter is provided that allows for visualization of the cluster structure at different levels of detail. Furthermore, we present a number of experimental results using standard data mining benchmark data.

Abstract-Self-Organizing Maps are a prominent tool for exploratory data analysis. In this paper, we propose a method of visualizing the cluster structure of the SOM based on the similarity of areas on the map, computed by aggregation of the distances of the underlying component planes of the codebook. The result can then be plotted on top of the map lattice with arrows that point to the closest cluster center, which is analogous to flow and vector field visualizations. A parameter is provided that allows fine-tuning of the granularity of the clustering, which can be adjusted according to whether a global or local view on the map is desired. We provide experimental results with a real-world data set where we discuss the effects of parametrization and the general applicability of our method, along with comparison to related techniques.

Abstract-Self-Organizing Maps are a prominent tool for exploratory data analysis. In this paper, we propose a method of visualizing the cluster structure of the SOM based on the similarity of areas on the map, computed by aggregation of the distances of the underlying component planes of the codebook. The result can then be plotted on top of the map lattice with arrows that point to the closest cluster center, which is analogous to flow and vector field visualizations. A parameter is provided that allows fine-tuning of the granularity of the clustering, which can be adjusted according to whether a global or local view on the map is desired. We provide experimental results with a real-world data set where we discuss the effects of parametrization and the general applicability of our method, along with comparison to related techniques.