论文信息 - On the Similarity of Eagles, Hawks, and Cows: Visualization of Semantic Similarity in Self-Organizin

On the Similarity of Eagles, Hawks, and Cows: Visualization of Semantic Similarity in Self-Organizin

We describe an extension to the self-organizing map learning rule enabeling a straightforward visual representation of input data similarity in high-dimensional input structures. The general idea of the extension is to mirror the movement of weight vectors during the training process within a two-dimensional (virtual) output space. The result of the extended training algorithm allows intuitive analysis of the similarities inherent in the input data and most important, intuitive recognition of cluster boundaries.

Andreas Rauber | Dieter Merkl

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