Ranked centroid projection: a data visualization approach for self-organizing maps

The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data as it performs a topology-presenting projection of the input space on a low-dimensional grid. To utilize the information provided by the SOM and obtain an approximation of the data structure, a separate data projection method is usually needed. However, most of the SOM projection methods are computationally expensive when the size of the data set becomes large. In this paper, we present an intuitive and effective SOM projection method with comparatively low computational complexity for the purpose of cluster visualization. This method maps data vectors on the output space based on their responses to different prototype vectors. High-resolution maps can be obtained with a relatively small network size. The proposed method is demonstrated using both an artificial and a real world data set.

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